"Brains Make Minds" Is Dumb, and "Brain Waves Make Minds" Is Even Dumber

 We have the strongest reasons for rejecting the dogma that brains make minds. Specifically:

• the fact that there are many dramatic cases in the medical literature of people who had more or less normal minds even though large fractions of the brain (or most of their brains) were destroyed due to injury or disease, including super-dramatic cases of people with good minds but less than 15 percent of their brains;
• the fact that there is no scientific understanding at all of how brains or neurons could be producing consciousness, thought, understanding or abstract ideas (mental things that are very hard or impossible to explain as coming from physical things);
• the fact that there is no plausible account to be told of how brains could possibly be storing memories that last for fifty years, given the high protein turnover in synapses, where the average protein only lasts a few weeks;
• the fact that there is no scientific understanding at all of how brains or neurons could produce any such things as choices or decisions;
• the fact that there is no understanding of how brains could achieve the instantaneous recall of distant, obscure memories that humans routinely show, given the lack of any coordinate system or addressing or indexing in a brain that might allow some exact position of a stored memory to be very quickly found;
• the fact that there is no understanding whatsoever of how concepts, visual information, long series of words, and episodic memories could ever be physically stored by a brain in any way that would translate all these diverse types of information into synapse states or neuron states;
• the fact that the microscopic examination of very many thousands of brains of recently deceased people (and the microscopic examination of endless samples of brain tissue extracted from living people) has never produced the slightest trace of learned information, something that would have been discovered in brains 50 years ago if brains stored memories and brains are the source of the human mind;
• the fact that human brains (all very severely handicapped by cumulative synaptic delays and unreliable synaptic transmission) are way too slow and way too noisy to explain the wonders of human best mental performances, which include endless wonders of blazing fast calculation, blazing fast precise recall, blazing fast memorization,  and the recitation with perfect accuracy of very long bodies of text consisting of hundreds of pages;  
• the fact that for more than 50 years numerous people have reported vivid near-death experiences and out-of-body experiences occurring after their hearts stopped and their brains were inactive, during times when their brains had flatlined, and they should have had no consciousness at all (under "brains make minds" assumptions), with many of the observation details they reported seeing during such brain-inexplicable should-have-been-utterly-unconscious experiences being independently verified (as described here);
• the fact that humans have very many types of well-documented experiences that are inexplicable under any claim that the brain is the source of the human mind.  

Given such realities, what is the intelligent direction to move in, to resolve the explanatory shortfall? That direction is to try to explain human minds and human memory by looking for something more than the brain. What is the wrong direction to move in, to resolve the explanatory shortfall? It is to try to explain the human mind by postulating something less than a brain. 

We have in the article here a discussion of neuroscientist named Miller  moving in such an "ass-backward" direction. It is an article entitled "MIT Neuroscientist Proposes Brain Waves are the Hidden Engine Behind Thought and Consciousness." You may start to realize how stupid this idea is once you realize that so-called brain waves are not a discrete thing that really always exist independently in the brain, but are instead a kind of measurement artifact conjured up by neuroscientists and neurologists, an artifact appearing only when a particular technological protocol is used. 

What are brain waves? They are the squiggly lines that a neuroscientist or neurologist gets when he puts on someone's head a device called an EEG headcap. Such a headcap is connected to a machine. The kind of setup is shown in the visual below:

Now, the waves shown on the computer screen are not discrete things that exist independently throughout your day-to-day existence. Such waves really only come into existence when a neuroscientist sticks one of these caps on someone's head and starts doing the reading. 

You really have every day all of these things:

• You have a brain. 
• You have neurons inside your brain. 
• You have synapses inside your brain. 
• You have electricity and chemicals traveling between different neurons of your brain.
• You have very many mental capabilities. 
• Your have a self.
• You have thoughts, feelings and experiences.
• You have memories, and various memory abilities, such as the ability to learn new things, and the ability to recall old memories. 

But today you do not really have any such thing as brain waves corresponding to the waves shown on EEG screens like the one shown above. It is possible for you today to conjure up such brain waves, by going to a doctor or neuroscientist, and having him put on your head an EEG cap like the one shown above. Or, if you paid for some at-home EEG-type device, you might be able to conjure up brain waves, by sticking such a device on your head, and seeing brain wave readings on a computer screen. 

A device that shows brain waves is one that engages in a largely arbitrary act of pictorial representation. To understand that, let's first look at the ground reality that is used as a starting point for the pictorial construction of brain waves. The brain consists mainly of neurons, and each neuron has a particular firing rate, with the firing rate varying from between 1 time a second to about 100 or 200 times per second. Over any time interval, the average firing rates of neurons can be measured.  So imagine a cube consisting of 100 smaller cubes, and imagine each of those cubes has in it a number, with the number varying over time. We can think of the numbers as being the average firing rates of one particular area in the brain. 

When an EEG headcap is put on someone's head, particular electrodes kind of line up with some of the outer parts of the brain. An analogy would be if you hooked up a wire to each of about 30 of the cubes shown in the diagram above. So you can get readings that correspond to variations of the average firing rates of particular parts of the brain. 

Such readings are not any direct capturing of a wave, comparable to a photo showing a wave on a beach. But the data received from such readings of brain firings can be visually depicted as a wave. You can plot changes in firing frequency over time, and depict that as a wave. Similarly, there are no real waves in the data of a baseball game. But you could visually plot some of that data as a wave. For example, you might have a "hits per inning" graph that used waves, as below. The graph shows how many many hits a particular team got during each of the nine innings in a baseball game. 

Baseball games don't really have "hitting waves" like shown above as parts of what happens inside them; baseball games only have events such as hits (singles, doubles, etc.) and outs (such as a strikeout or a force out). But by graphing the data in a particular way, you can construct a "hitting wave" graph. Similarly, brains don't really have "brain waves," but you can graph data on fluctuations in brain firing rates, in a way that shows waves. "Hitting waves" would be an analytic construction of baseball analysts, and "brain waves" are an analytic construction of brain analysts. 

From this discussion we can start to realize how silly is the notion suggested by some MIT scientist that "brain waves are the hidden engine behind thought and consciousness." Strictly speaking, you don't really even have in your brain today any such thing as "brain waves," and so-called "brain waves" are only what appears after some technology is used to graph  fluctuations in brain firing rates. 

An ocean wave is a real thing that requires no arbitrary special processing to create. When you see something like what is shown below, that does not involve any "try to show a wave" algorithm. 

But when brain waves are shown on electronic screens connected to EEG devices, that does involve a special arbitrary  "try to show a wave" algorithm. 

In the article mentioned above, the neuroscientist makes these full-of-falsehoods claims about brain waves, which appear as consecutive statements. 

• "The brain uses these oscillatory waves to organize itself.” No, brain waves do not do anything at all to organize brains. 
• “Cognition is large-scale neural self-organization." No, cognition is mental, and not any physical organization. 
• "The brain has got to organize itself to perform complex behaviors. Brain waves are the patterns of excitation and inhibition that organize the brain, and this leads to consciousness because consciousness is this organized knitting together of the cortex.” No, brain waves do not do anything to organize any part of the brain, and consciousness is not any such thing as an "organized knitting together of the cortex."

The article makes this untrue boast with no basis in fact: "Over three decades of research in Dr. Miller’s lab at MIT have demonstrated how these waves help organize information flow across the cortex—the outermost layer of the brain responsible for higher cognitive functions." Brain waves are an epiphenomenon of electrical activity in the brain, comparable to the scent that arises when you cook a meal. Brain waves no more organize information than the scent you make when cooking organizes the meal you are cooking. 

Later the author of the article makes all kinds of claims about Miller's research that have no basis in fact. There is no robust research backing up any of the author's statements below:

"His work suggests that brain waves act like traffic signals for thought: slower 'top-down' frequencies carry goals and rules, while faster waves deliver sensory information. Together, they guide what we perceive, remember, and decide."

This is as groundless as claiming that ocean waves carry philosophical thoughts, and that the scent from your cooking carries rules about how you cook.  The word "guide" is one of the most abused words in science writing. People writing about biology are constantly claiming without adequate warrant that this or that chemical or mechanical effect "guides" something or "regulates" something, using statements that they have no warrant for using. The author is making the most groundless and senseless speculations when he says things such as "In essence, the balance between these wave patterns determines when certain thoughts emerge and when they remain suppressed." 

The theory that brain waves create minds is disproven by the reality of near-death experiences during cardiac arrest, when the brain's electrical activity shuts down, and brain waves flatline. While the brain is electrically shut down during cardiac arrest, many people have the most vivid experiences, which they remember very well. This proves that neither brains nor brain waves are necessary for vivid experiences involving memory. 

The term "isoelectric" or iso-electric in reference to brain waves means a flat-lining equivalent to no electrical activity in the brain, as measured by EEG readings. The paper here states, "Within 10 to 40 seconds after circulatory arrest the EEG becomes iso-electric." Figure 1 of the paper here says that such an isoelectric flat-lining occurred within 26 seconds after the start of ventricular fibrillation, the "V-fib" that is a common cause of sudden cardiac death, with "cortical activity absent." Also referring to a flat-lining of brain waves meaning a stopping of brain electrical activity, another scientific paper says, "several studies have shown that EEG becomes isoelectric within 15 s [seconds] after ischemia [heart stopping] without a significant decrease in ATP level (Naritomi et al., 1988; Alger et al., 1989)."  Another paper tells us this about brain waves and infarction (obstruction of blood flow), using CBF to mean cerebral blood flow, and the phrase "the EEG becomes isoelectric" to mean a flat-lining of brain electrical signals:

"When normal CBF declines, the EEG first loses the higher frequencies (alpha and beta bands), while the lower frequencies (delta and theta bands) gradually increase. When the CBF decreases further towards an infarction threshold, the EEG becomes isoelectric." 

Similarly, another paper refers to blood pressure, and tells us, "When flow is below 20 mL/100 g/min (60% below normal), EEG becomes isoelectric." meaning that brain electrical activity flat-lines. The 85-page "Cerebral Protection" document here states, "During cardiac arrest, the EEG becomes isoelectric within 20-30 sec and this persists for several minutes after resuscitation." Another scientific paper states this: 

"Of importance, during cardiac arrest, chest compliance is not confounded by muscle activity. The EEG becomes isoelectric within 15 to 20 seconds, and the patient becomes flaccid (Clark, 1992; Bang, 2003)."

For example, below is part of Figure S1A from the supplemental information of a scientific paper. We see the brain waves of a dying Patient One in blue (EEG readings), and we see in the last row a red ECG reading that is a  measure of heart activity.  

What people recall during near-death experiences in which their hearts have stopped and their brain waves have stopped are not some shadowy experiences only a tiny fraction as real-seeming as normal experiences. Instead, people having near-death experiences often report experiences that seemed more real and vivid than anything they have ever experienced. When people have such experiences in brains that are flatlining, with the brain waves shut down, that shows that brain waves are not any source of the human mind. 

Contrary to "Brains Make Minds" Claims, Brains Are Not Much More Electrically Active When You Are Awake

Those who claim that the brain makes the mind keep trying to push the silly idea that you are just a bunch of neural signals passing around inside your head. The scientists who make such claims typically are members of a belief community, a kind of sect of the ivory towers. When we hear such claims we are observing the speech customs of such a community. The members of belief communities often keep repeating the same old claims, which often are not justified by any robust evidence. 

It is interesting to consider this question: what would we expect if minds are produced by the mere firing of neurons? Three predictions would seem to follow from such an idea:

(1) If minds are produced by firing neurons, we would expect that neurons would fire much more frequently during conscious awareness than during unconscious sleep.

(2) If minds are produced by firing neurons, we would expect that neurons would fire much more frequently during heavy mental activity such as deep concentration, heavy calculation or rapid memory recall, than during a passive awake condition involving a mental resting state.

(3) If minds are produced by firing neurons, we would expect that when neurons fire most rapidly, that would produce the highest state of consciousness or mental activity. 

None of these predictions turns out to be true. To investigate this matter, you should ignore the type of visual shown below, a misleading visual that open appears in articles about the brain. The reality is that all of these types of brain waves occur during each of the listed states. 

A misleading diagram recurring in neuroscience articles

Figure 1 of the paper here ("Firing rates of hippocampal neurons are preserved during subsequent sleep episodes and modified by novel awake experience") shows two scatter plots showing neural firing rates in the hippocampus of rats. One scatter plot is marked "Awake" and the other scatter plot is marked "Sleep." The two scatter plots look pretty identical. They both show rates of neuron firing varying from about once every ten seconds to ten times per second. 

Judging from such graphs, neurons do not seem to fire more often in rats when they are awake. Some people claim that neurons fire less frequently during sleep, but they typically fail to give us specific figures as to how much less frequently they fire. 

Referring to readings from an EEG (a device that reads brain waves), we read this on an expert answers site:

"In REM sleep, the EEG is remarkably similar to that of the awake state (Purves et al., 2001). Although the EEG represents the synchronized activity of many neurons in the cortex, it does give us a clue whether they are firing faster or not. Wakefulness is mainly dominated by beta and gamma waves (source: Scholarpedia), i.e. 12 - 100 Hz. REM sleep is characterized by low-amplitude mixed-frequency brain waves, quite similar to those experienced during the waking state - theta waves, alpha waves and even the high frequency beta waves more typical of high-level active concentration and thinking, i.e. 4-30 Hz (table 1) (source: Sleep)."

When I ask Google "how much do average neuron neuron firing rates vary between sleep and wakefulness," I get the AI overview answer below:

"Average neuron firing rates decrease by approximately 30-40% during non-REM (NREM) sleep compared to wakefulness, largely driven by the appearance of 'OFF' periods (silence)...Similar to active wakefulness, neural firing rates in REM are generally higher than in NREM and often match awake levels."

This is an indication of only a small difference in neuron firing rates between sleep and wakefulness. We are told that during one type of sleep (NREM sleep) firing rates decrease by 30%, but that is a gradual decrease occurring over an hour or two. So if you had a gradual decrease to a 30% lower firing rate during  NREM sleep, this would be something like an average neuron firing rate of 15% less than during wakefulness. And during the other type of sleep (REM sleep)  neurons seem to fire about as often as when you are awake. 

Diving into this AI overview by doing more scrolling or clicking, I find the story changes. Later in the same AI overview I am told that "average neuron firing rates vary significantly between sleep and wakefulness, typically characterized by a 10%–20% decrease during sleep, though these shifts depend heavily on the specific brain region and sleep stage." 

The type of graph that gives you the most information when analyzing brain waves is a type of graph called the EEG multitaper spectrogram. Someone unfamiliar with it may have to take a minute or two studying how the graph works before he can understand it. The graph can show up to 10 hours of brain activity. Each column of pixels shows the activity for a particular short time unit such as a minute or a few minutes. The higher rows on the graph represent the higher-frequency brain waves. A red color represents a high intensity; a yellow or green color represents a medium intensity; and a blue color represents a lower intensity. 

We are sometimes shown versions of this graph which will suggest that lower-frequency brain waves are much more common during sleep. However, in Figure 7 of the paper here, we are shown  multitaper EEG spectrograms that are called representative of sleep, and those diagrams seem to depict theta, alpha and beta waves occurring almost as frequently as delta waves. 

The paper "Sleep Neurophysiological Dynamics

Through the Lens of Multitaper Spectral

Analysis" by Prerau et. al. seems like the best paper I can find giving data comparing brain waves during sleep and brain waves when  awake. The paper has many examples of EEG multitaper spectrograms plotting the differences between brain waves during sleep and brain wave when awake.  The graphs show no strong evidence of greater electrical activity in the brain while you are awake. 

Here is figure 1 of the paper, showing electrical activity in a brain from midnight to 10:00 AM. Roughly the first hour and the last hour are wakefulness. 

I can give some tips on interpreting this graph:

(1) The graph plots brain waves that occurred over about 10 hours that included 8 hours of sleep and 2 hours of being awake. 

(2) The left edge of the graph plots brain waves occurring during an hour of being awake, before the 8 hours of sleep occurred. 

(3) The right edge of the graph plots brain waves occurring during an hour of being awake, after the 8 hours of sleep occurred. 

(4) The middle 80% of the graph plots brain waves occurring during sleep. 

(5) With this type of graph, the redder the color and the higher up on the graph the colors occurs, the greater the indication that more electrical activity was occurring. For any given frequency, blue represents the lowest power; green represents a higher power than blue; yellow represents a higher power than green; and red represents a higher power than yellow. 

So what do we see in the graph above? Overall, there is little difference between the amount of electrical activity occurring during sleep and while being awake. According to the graph, while awake there is a slightly greater power in the higher frequency band (about 15 Hz), because the top left corner and the top right corner are a little more green than blue.  But according to the same graph while the person is awake there is slightly less power in the lower frequency band (about 7 Hz), because in the bottom left and the bottom right of the graph we see more green and yellow than red. Overall, we seem to see no evidence of much greater electrical activity in the brain when a person is awake, compared to when he is asleep. 

What we see here is evidence suggesting that brains are not much more electrically active when you are awake as opposed to when you are asleep. This isn't what we would expect under the dogma that the brain is the source of the mind. 

Another interesting comparison to make is to compare brain waves during wakefulness and brain waves during anesthesia. The average person might think that the firing rate of neurons slows down greatly during the unconsciousness produced by anesthesia. That is not true, however. 

The diagram below is from the paper " Electroencephalographic dynamics of etomidate‐induced loss of consciousness" that you can read here. At the bottom we see an EEG multitaper spectrogram showing brain waves during both a conscious awake state and unconsciousness produced by anesthesia. The first third of the bottom visual shows the awake and conscious state. According to the diagram, loss of consciousness (LOC) occurs at around the 300 second mark, about the middle of the colored visual. 

There is no reduction in brain activity or brain firing rates documented by the diagram. In fact, the caption of the paper says, "Compared with those during the awake period, the powers of the slow wave (< 1.0 Hz), delta wave (1.0–4.0 Hz), theta wave (4.0–8.0 Hz), and alpha wave (8.0–13.0 Hz) during the etomidate-induced LOC [loss of consciousness] were significantly increased (C: 0–22.97 Hz, 27.28–40.00 Hz; p < 0.001, two-group test for spectra)." Notice that at the point marked LOC in the figure (which stands for Loss of Consciousness), we see no real change in the brain activity. Injecting the anesthetic produces a fairly small change, but at the time when the consciousness is lost, brain activity does not change. 

This is just as we would expect under the idea that your brain is not the source of your mind. 

Science Literature Is Full of Misleading Claims About Brain Waves

Many neuroscientists mislead with colors, and some other neuroscientists mislead us with lines. I give a detailed explanation of how neuroscientists so often mislead us with colors in my post here.  I can summarize that post in a single paragraph. What goes on is that neuroscientists do brain imaging studies attempting to show what are called neural correlates of particular mental activities. The neuroscientists are hoping to find evidence that some particular part of the brain will become much more active during some mental activity. With the exception of activity in the occipital part of the brain (which is more active when people are seeing things), no such evidence of much greater activity is found. Typically the brain imaging will only show some brain region being only about 1 part in 200 more active, a half of one percent more active, no more than we would expect to see by chance fluctuations.  But what goes on is that such very tiny "half of one percent" variations will be depicted with brain imaging visuals showing the tiny fluctuations appearing in bright red against a black-and-white background. Such visuals deceive us by giving the idea that a large variation has occurred. If the visuals were to be honestly done, they would show variations in color so small you never would be able to notice them. Human brains don't look or act significantly different when you think or imagine or remember, contrary to claims that such things are neural activities. 

Now let me explain how so many neuroscientists mislead us with lines and mislead us about lines. This goes on when neuroscientists give us misleading visuals regarding brain waves, and make misleading statements about brain waves. Below is a type of chart that we see very often in the literature of neuroscience, mostly in popular accounts. As I note at the bottom, this type of diagram is misleading, because all five of these types of brains waves show up in all of the different states listed.

Claims matching the claims above are made in popular literature and also in some very misleading papers written by neuroscientists. This is just another example of neuroscientists making claims in their papers that are not justified by observations, something that occurs massively. An example of such a misleading paper is the paper "Changes of the brain’s bioelectrical activity in cognition, consciousness, and some mental disorders." The authors make generalizations about brain waves and cognitive activity that are not justified by any research they cite. The misleading generalizations the authors make in Table 1 of their paper (and the paragraph preceding it) are repeated word-for-word in the wikipedia.org article on Electroencephalography.

Below are some reports from the neuroscience literature, reports that conflict with diagrams like the one above, and conflict with the paper cited above, by showing that all the main types of brain waves occur during the main types of neural activity:

Delta Waves (1-4 Hz)

• "This wave is recorded during very low activities of the brain and deep sleep (link)."
• "Delta waves (DW) are present both during sleep and in wakefulness (link)."
• "Our recordings reveal rhythmic delta during wakefulness at 10% of all recording sites" in 18 humans (link). 
• "Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness (link)." 
• The diagram here shows many delta waves (1-4 Hz) occurring abundantly in animal brains while animals "waited to see a new image." 
• The paper here has a graph showing delta waves (1-4 Hz) occurring abundantly while humans meditated. 
• A paper on epileptic seizures says, "Slow waves (≤4 Hz) can be found in seizures with impairment of consciousness and also occur in focal seizures without impairment of consciousness but with inhibited access to memory functions."
• The paper "Brain Activation and Cognitive Load during EEG Measured Creativity Tasks Accompanied by Relaxation Music" has circular "power spectral analysis" charts for six subjects, showing mostly delta and theta waves (about 5 Hz) for subjects while they were doing three things: resting, doing creative work, and speaking.  
• The paper "IDENTIFICATION OF DOMINANT WAVE DURING THE RECITATION OF AL-MULK VERSE WITH (WITHOUT) UNDERSTANDING USING EEG SIGNAL" reports "During Al-Quran recitation (without understanding), the highest amplitude of the power spectrum distribution was observed in Delta at the Frontal area (F1, F2, F4, F7 and F8), and Beta2 and Gamma on EEG, predominantly in the T3, T4 and T5 area." This is an example of what occurs very frequently: which brain wave is dominant will vary depending on which brain region is read from. 

Theta Waves (4-8 Hz)

• "In anxious individuals for example, research suggests that there is a significantly higher degree of frontal-midline theta activity compared to non-anxious individuals, and that could be associated with more reactive control modes of behavior in the moment rather than proactive behaviors such as planning and preparation (link)."
• "This rhythm is recorded during low brain activities, sleep, or drowsiness (link)."
• "Theta waves generate the theta rhythm, a neural oscillation in the brain that underlies various aspects of cognition and behavior, including learning, memory, and spatial navigation in many animals (link)."
• The diagram here shows many theta waves (4-8 Hz) occurring in animal brains while animals "waited to see a new image."
• The paper here has a graph showing theta waves (4-8 Hz) occurring abundantly while humans meditated.
• A scientific paper states that "REM sleep is characterized by frequencies in the theta (4–8 Hz), beta (16–32 Hz), and gamma (>32 Hz) ranges."
• The paper here has a graph (Figure 8) showing theta waves (4-8 Hz) occurring abundantly in people playing a throwing game.
• The paper "Brain Activation and Cognitive Load during EEG Measured Creativity Tasks Accompanied by Relaxation Music" has circular "power spectral analysis" charts for six subjects, showing mostly delta and theta waves (about 5 Hz) for subjects while they were doing three things: resting, doing creative work, and speaking.
• The paper here found quite a bit of alpha, theta and gamma waves during a memorization test.

Alpha Waves (8-13 Hz)

• The paper here has a graph showing alpha waves occurring abundantly while humans meditated.
• The diagram here shows many alpha waves (8-13 Hz) occurring abundantly in animal brains while animals "waited to see a new image."
• The paper here has a graph (Figure 8) showing alpha waves (8-13 Hz) occurring abundantly in people playing a throwing game.
• In Figure 1 of the paper here, we are shown  multitaper EEG spectrograms that are called representative of sleep, and those diagrams seem to depict theta, alpha and beta waves occurring almost as frequently as delta waves.
• The paper here refers to alpha waves occurring during anesthesia, when a patient is in deep unconsciousness. We read, "All four spectrograms for these data show the well-known alpha-beta oscillations (8–17 Hz) and slow-delta oscillations (0.1–4 Hz) that are characteristic of general anesthesia maintained by sevoflurane." 
• The paper here found quite a bit of alpha, theta and gamma waves during a memorization test.
• While the page here claims that alpha waves "disappear during sleep," the page here states that "stage 1 sleep is associated with both alpha and theta waves," and shows an EEG of alpha waves recorded during sleep.  The page here states that alpha waves of between 8 to 14 Hz occur in "bursts of activity" in stage 2 sleep (light sleep). 
• A paper studying brain waves during hypnosis in 8 subjects found little change in brain waves, with alpha waves being the main type of wave before, during and after hypnosis. Conversely, another paper tells us "a number of studies have not found an increase in alpha activity with hypnosis (Kihlstrom, 2013; Ray, 1997; Sabourin, Cutcomb, Crawford, & Pribram, 1990)," and it also tells us "findings linking hypnosis to theta oscillations, however, are more common."

Beta Waves (13-30 Hz)

• The first graph below show gamma waves while animals "waited to see a new image."
• The paper here has a graph showing beta waves occurring abundantly while humans meditated.
• An article in an encyclopedia of neuroscience states, "Beta and gamma waves (20–80 Hz) occur spontaneously during REM sleep and waking and are evoked by intense attention, conditioned responses, tasks requiring fine movements, or sensory stimuli."
• In Figure 1 of the paper here, we are shown  multitaper EEG spectrograms that are called representative of sleep, and those diagrams seem to depict theta, alpha and beta waves occurring almost as frequently as delta waves.
• The paper here has a graph showing beta waves occurring in significant amounts during anesthesia.
• The paper here has a graph (Figure 8) showing beta waves (13-30 Hz) occurring abundantly in people playing a throwing game.

Gamma Waves (30-100 Hz)

• The first graph below show gamma waves while animals "waited to see a new image."
• The paper here has a graph showing gamma waves occurring abundantly while humans meditated. Despite some claims of gamma wave activity being associated with concentration, a study of brain-injured veterans found they have much higher levels of gamma waves in a certain brain region. 
• A paper found that "gamma oscillations in low (30–50 Hz) and high (60–120 Hz) frequency bands recurrently emerged in all investigated regions" in the brains of 20 humans during "slow wave sleep."
• A paper on sleeping monkeys says, "Gamma oscillations around 50 Hz or higher were most prominently observed during REM sleep."
• An article in an encyclopedia of neuroscience states, "Beta and gamma waves (20–80 Hz) occur spontaneously during REM sleep and waking and are evoked by intense attention, conditioned responses, tasks requiring fine movements, or sensory stimuli."
• A scientific paper states that "REM sleep is characterized by frequencies in the theta (4–8 Hz), beta (16–32 Hz), and gamma (>32 Hz) ranges. "
• Another paper found gamma wave activity (in the range of 30-40 Hz) in hypnotized subjects.
• A paper states that "gamma oscillations (30-50 Hz) recorded in the local field potentials (LFP) of the hippocampus are a marker of temporal lobe seizure propagation," and that "78.2% of seizures involving both the hippocampus and amygdala showed hippocampal gamma oscillations," conflicting with claims that such gamma waves (gamma oscillations) are characteristic of problem solving or concentration. 
• The paper here found quite a bit of alpha, theta and gamma waves during a memorization test. 
• The source here states that "some researchers contest the validity or meaningfulness of gamma wave activity detected by scalp EEG, because the frequency band of gamma waves overlaps with the electromyographic frequency band," so  "gamma signal recordings could be contaminated by muscle activity."

Graphs Plotting Multiple Brain Wave Types During a Single Activity

One way to get a clearer idea about such matters is to look for papers that plot multiple brain wave types during a single type of mental activity. You can find some papers of this type by doing a Google search for "brain wave power frequency." For example, the link here takes to a press release for a study with a line graph that plots all types of brain waves seen while animals "waited to see a new image."  We see the diagram below:

The graph shows brain waves from three different parts of the brain. The graph shows plenty of all five of the main types of brain waves: delta waves, theta waves, alpha waves, beta waves and gamma waves. The graph does not match the depiction in the typical brain wave chart, suggesting delta waves only occur during sleep. 

The paper here shows a similar graph of brain waves arising when humans meditated. Delta waves, theta waves, alpha waves, beta waves and gamma waves all occur in pretty much equal abundance. The paper here shows a similar graph arising with three subjects in a resting state (awake but eyes closed) and one subject looking at a blue "O" in front of him. All had delta, theta, alpha and beta waves in roughly equal amounts. 

The paper here shows (1) a circular graph of brain waves arising when six humans were in a resting awake state; (2) a circular graph of brain waves arising when six humans were doing creative activity  (3)  a circular graph of brain waves arising when six humans were speaking. There is little different in the three graphs. They all show theta waves of about 5 Hz as being by far the most common brain wave during all of these mental states, with beta waves and gamma waves occurring much less frequently. 

There is a type of graph called a multitaper EEG spectrogram. Someone unfamiliar with it will have to take a few minutes studying how the graph works before he can understand it. The graph can show up to 8 hours of brain activity. Each column of pixels shows the activity for a particular short time unit such as a minute. The higher rows on the graph represent the higher-frequency brain waves. A red color represents a high intensity; a yellow or green color represents a medium intensity; and a blue color represents a lower intensity. 

We are sometimes shown versions of this graph which will suggest that lower-frequency brain waves are much more common during sleep. However, in Figure 1 of the paper here, we are shown  multitaper EEG spectrograms that are called representative of sleep, and those diagrams seem to depict theta, alpha and beta waves occurring almost as frequently as delta waves. 

In a Dream Catcher study described here and the 2020 scientific paper here, EEG recordings were made of subjects while they were sleeping. The subjects were awakened at random times, and asked to tell whether or not they were dreaming.  Then some scientists ("blind" to which EEG readings were from the dreamers) were asked to guess whether particular subjects were dreaming. The result was a null result. There was no evidence that by studying EEG recordings you can tell whether a person is dreaming.  

Scientists apparently delayed the release of these results for years. A 2015 paper describes results just like those of the Dream Catcher study, but results that had apparently not yet been published:

"When data from serial awakenings of 9 subjects had been collected, these data were divided. Introspective reports and electroencephalographic recordings were analysed by different judges who were ignorant of which EEG sequences had led to dream reports and which ones had not. An external EEG research group used a number of statistical methods to identify the signature of the recordings that were followed by dream reports. But the accuracy of their predictions turned out to be no better than chance. A doctoral researcher presenting these findings at a conference explained that there were 4 different explanations for this failure: ‘Subjective experience is a) not in the brain, b) is in the brain, but not in the EEG, c) is in the EEG, but not in our data, or d) is in the data, but needs more complex and novel methods of analysis.’" 

The paper then quotes someone from 2008 saying this:

"We still haven’t found any objective sign indicating the presence or absence of consciousness in the dreaming brain. Maybe that’s something that Descartes would have predicted: that you cannot objectively capture consciousness because it is this immaterial, non-spatial, and imperceptible thing ... We haven’t been able to disprove the Cartesian position ... The dream catcher experiment is a test of the whole emergent materialist position ... We will continue our analysis, but if we can’t find anything then we have a real problem where to go."

Brain Waves During Hypnosis

A paper studying brain waves during hypnosis in 8 subjects found little change in brain waves, with alpha waves being the main type of wave before, during and after hypnosis. A study with a much larger study group size (32 subjects) found little change in brain wave activity during hypnosis. The paper ("An Investigation of Changes in Brain

Wave Energy during Hypnosis with Respect to Normal EEG") states this:

"We have found significant changes in the delta and beta band relative energy in channel C3. But the results of the statistical analysis show that the changes of the energy in the other frequency bands and also in the other channels are not significant."

Hypnosis involves dramatic changes in human consciousness.  The topic of changes in human pain perception, mental abilities and suggestibility during hypnosis is itself a huge topic with a very large literature.  The fact that something (hypnosis) involving so large a change in mental states seems to involve so little a change in brain waves is one that helps to undermine claims that minds come from brains. 

Trying to Predict Personality From Brain Waves

An interesting paper with a sample size much larger than in most neuroscience papers is the paper "Personality cannot be predicted from the power of resting state EEG." We read this:

"EEG was recorded from a large sample of subjects (n = 289) who had answered questionnaires measuring personality trait scores of the five dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data."

Conclusion

Neuroscience literature very often makes untrue simplistic generalizations about brain waves, statements suggesting incorrectly that particular types of brain waves only occur during particular types of activities. In general, with a few scattered exceptions, there is no close correspondence between brain waves and particular types of mental activities and mental states. Scientific studies and popular articles on this topic make extensive use of cherry-picking, in which signal dominance will be reported with some activity, based on readings from only one part of the brain. Typically a reading of brain waves from all parts of the brain will show a mix of most types of brain waves occurring during most types of mental activities. In general, brain waves are not signatures of some particular type of mental activity. 

Frequent claims that brain waves play a role in memory or learning are without foundation, and the evidence is consistent with such waves being merely an epiphenomenon of neural activity (like the scent arising from cooking soup). Brain waves show no sign of the signal modulation that characterizes information signals (like the signal modulation we see in man-made radio waves). 

Some Brain Wave Analysts Are Like "Face of Jesus in My Toast" Claimants

The site www.neurosciencenews.com is a frequent supplier of dubious brain-related stories, very many of which start out with unfounded headlines not matching anything that was actually observed. The site's latest not-really-true headline is one proclaiming "First Evidence of Replay During Sleep in the Human Motor Cortex, Which Governs Voluntary Movement." As a general rule, you should tend to be suspicious of anyone claiming to provide the first evidence of something, particularly anything having to do with the brain and the mind.  

The article attempts to persuade us that while someone was sleeping, his brain was replaying some memory of a motor skill that the person had recently learned.  The article refers to a scientific paper that provides no robust evidence of such a thing, providing no justification for its title of "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep." The paper is another study guilty of Questionable Research Practices, which are epidemic these days in experimental neuroscience.   The paper gives us another example of what is going on very frequently in neuroscience research these days: scientists making claims in titles and abstracts that are not justified by any observations described in the paper. 

Here is a quote from the www.neurosciencenews.com article. An extremely dubious speculation is passed off as a "discovery," but while describing this supposed "discovery" the text admits what is going on is "theorizing," something better described as mere speculation. 

"Scientists studying laboratory animals long ago discovered a phenomenon known as 'replay' that occurs during sleep, explains neurologist Daniel Rubin, MD, Ph.D., of the MGH Center for Neurotechnology and Neurorecovery, the lead author of the study.

Replay is theorized to be a strategy the brain uses to remember new information. If a mouse is trained to find its way through a maze, monitoring devices can show that a specific pattern of brain cells, or neurons, will light up as it traverses the correct route.

'Then, later on while the animal is sleeping, you can see that those neurons will fire again in that same order,' says Rubin. Scientists believe that this replay of neuronal firing during sleep is how the brain practices newly learned information, which allows a memory to be consolidated—that is, converted from a short-term memory to a long-term one."

No such "replay" was ever discovered. What is going on seems to be simply pareidolia, which is when people eagerly seeking some pattern claim that they have detected such a pattern, like someone checking his toast every day eagerly looking for the face of Jesus, and one day reporting that he finds a piece of toast that looks like Jesus. 

Let's imagine some society dedicated to showing that the clouds above us contain the ghosts of dead animals. Given many eager  researchers who scan the clouds day after day looking for shapes that look like the shapes of animals, such a society would probably be able to report some successes, finding a few clouds that look like animals. Similarly, let us imagine some experimenters want to show that some brain activity occurring during some motor activity is "replayed" during sleep. Given eight hours of recordings of brain activity during sleep, it will not be too unlikely that such experimenters would report that sometime during sleep there was some brain wave activity that looked like the brain wave activity that occurred when the motor activity occurred when a subject was not asleep. 

Below are some of the things that can help you sort out whether or not robust evidence has been provided:

(1) Look for adequate sample size.  If a study used 15 or more subjects per study group, it is a good sign that the study may have used an adequate sample size.  The  "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper uses the grand total of only one subject. 

(2) Look for an adequate number of control subjects.  A well-designed study will use an adequate number of control subjects. We can imagine how control subjects could have been effectively used in a study like this.  Brain waves could have been read from 30 subjects, 15 of whom had learned something, and 15 of whom had not learned that thing. But the "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" does not mention any control subjects. All measurements seemed to have occurred from only one subject.

(3) Look for a well-designed blinding protocol.  A well-designed study will use a blinding protocol designed to minimize the chance that researchers will observe and analyze data in a biased way to get whatever result they are hoping to get. The "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper does not mention any blinding protocol or blinding procedure. If the study had been done properly, analysts would have been blind as to whether brain waves they were analyzing came from the control subjects who had not learned the motor skills that were supposedly "replayed" or from subjects who had learned such skills. 

(4) Look for pre-registration.  With a pre-registered study, scientists commit themselves to one particular way that data will be gathered and analyzed, a method publicly committed to before any data is gathered. When pre-registration is not used, we should always be suspicious that scientists have simply "sliced and diced" data in as many ways as they wanted, until it coughed up something maybe looking a little like the desired effect.  The use of pre-registration minimizes the chance that a scientific paper is a kind of "keep torturing the data until it confesses" affair.  The "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper is not a pre-registered study. 

(5) Look for a statement of an effect size.  When robust evidence has been found, researchers will typically report an effect size. The "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper does not report any effect size. 

We can imagine what a study might look like if it were to show convincing evidence of neural replay during sleep of a learned muscular behavior. We might see one long squiggly line showing a brain wave recorded when the muscular activity was occurring when the subject was awake. We might then see another long squiggly line showing a brain wave recorded at some point during sleep. The two wiggly lines (each with many up crests) might match exactly, in a way that might be unlikely to occur unless the brain was replaying a muscle memory.  We would see in the paper an impressive visual showing one long wiggly brain wave line exactly matching another long wiggly brain wave line. The "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper has no such visual. 

In the section of the paper entitled "Quantification and Statistical Analysis," we have a "jargon gobbledygook" description of the tortuous statistical rigmarole that went on, a section that might have honestly been labeled "Desperately Seeking Replay Evidence." Below is a description of only part of the byzantine "Rube Goldberg machine" statistical maneuvering that was occurring:

"We used these templates to probe for evidence of replay during the resting and sleeping epochs as follows. At each time-step of the neural recording, for each of the two spatial dimensions, we calculated the cross-correlation between the template and the output of the steady-state Kalman filter. This process yielded two time-series of correlation coefficients equal in length to the time series of the neural recording. Separately for the X- and Y-dimensions, the 98th (for Session 1) or 99th (for Session 2) percentile of the correlation coefficients was chosen as the threshold to designate an activity pattern as a template match. We designated instances when the correlation crossed threshold in both the X- and Y-dimensions simultaneously as simultaneous threshold crossing events (STCEs). STCEs occurring over neighboring time-steps are classified as a single event. STCEs occurred tautologically during the awake task performance blocks. When occurring during rest or sleep, we refer to these instances as putative replay events. The specific percentile implemented as the threshold for a session was selected to optimize the performance of STCEs to correctly identify successful target trials and not identify unsuccessful target and all distractor trials during the active task performance. This was quantitatively operationalized by finding the integer percentile that jointly maximized the sensitivity and specificity (i.e., the Youden's J statistic: sensitivity + specificity − 1) of STCEs to accurately identify successful target trials during awake task performance. To evaluate whether there is relative preservation of neuronal firing sequence during these putative replay events, we determined the order of neuronal firing during each successful target trial and each putative replay event by calculating, for each channel (representing the single or multiunit activity recorded), the time bin within 4 s after the onset of the task completion or replay event that had the maximum firing rate. Thus, for each event (task performance or putative replay), a 96-element sequence was identified. To determine the preservation of firing order across events, we calculated the pairwise matching index Im between each task completion and putative replay event, using the approach derived by Ji and Wilson (2007) where Im is defined as follows. For an M-channel recording, there are M(M – 1)/2 pairs of channels; between two events, let m be the number of pairs that have the same order of peak firing between the two events, and n be the number of pairs that have the opposite order. Define Im = (m – n)/(m + n), such that Im is bounded by [−1, 1]. Two events with precisely the same sequence of activation will have Im = 1 and two events with exact opposite order of activation would have Im = −1. To determine whether the distribution of matching indices we observe are greater than would be expected by chance, we generated a control distribution by calculating the matching indices of 100,000 pairs of randomly generated 96-element-long sequences...We randomly selected 100 segments of neural activity, each equal in duration to the successful target sequence templates, from the 30 min period of rest recorded immediately before the task blocks. We used the output of the Kalman filter generated by these randomly selected segments to produce a series of 100 pseudo-templates. For each pseudo-template, we repeated the template-matching procedure described above, calculating the cross-correlation between the template and the Kalman filter output at each time-step of the recording, and counted the number of STCEs for each pair of pseudo-templates. Because the generated distribution was highly left-skewed, we used a Wilcoxon rank-sum test to compare this distribution with our observed outcome from the true target template to determine whether the number of observed putative replay events was greater than would be expected by chance (i.e., compared with the distribution of STCEs produced from the pseudo-templates). Because the random 'pseudo-template' control described above did not necessarily preserve neural firing rate statistics, as a second control, we performed an alternative bootstrap procedure that specifically preserved the statistics of the underlying neural firing. In this control, for each of 100 iterations, the Kalman filter output for the duration of the recording was broken into 5 min segments. Within each segment, we used the discrete Fourier transform to randomize the phase of the X and Y dimension of the Kalman filter output. We then reassembled the segments into full-time-series and performed the same cross-correlation matching procedure described above using the true target templates....To assess for neuronal replay of target trajectories at different speeds, for each recording session, we used cubic splines to fit the pair of target Kalman filter trajectories and then adjusted the duration of the template using a temporal dilation/compression factor we define as τ. We varied τ over 18 values from 0.1 to 10. For each value of τ, the number of STCEs was calculated during each epoch as above. We ran the phase-randomized bootstrapping control at each value of τ to assess for statistical significance."

I can give an analogy for what seems to be going on above. Although I have never used any version of the Photoshop software, I hear that it has many utilities called filters that allow you to make various transformations of images.  Imagine if someone kept photographing his toast, but never seemed to get an image of Jesus. He might try playing around with Photoshop filters, subjecting each toast photo to many types of filters, until he finally got something that looked a little like Jesus. That would be rather like what is going on in the "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper.  Sifting through hours of brain wave recordings of a sleeping subject, the authors seem to have played around with strange statistical manipulations until they got something that they can claim as some evidence of a brain replaying a memory during sleep. It seems like a "keep torturing the data until it confesses" kind of affair. 

The "so many zig-zags" statistical procedure described in the "Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep" paper is so complicated a "hall of mirrors" with so many "madhouse rules" with a "make it up as you go along" kind of stink that no one will ever be able to impressively reproduce it using the same procedure, nor will the authors ever be able to justify the strange arbitrary analysis choices they made (when using an algorithm like a huge vat of  tangled spaghetti). "Going deep down the rabbit hole" like that isn't sound experimental science, which generally involves straightforward well-justifiable procedures to yield reproducible results. The authors have not provided any robust evidence at all of brains replaying memories during sleep. 

The Neuroscience News site at www.neurosciencenews.com has for very long been guilty of publishing unfounded headlines that are not justified by any research discussed in the story below the headlines. Besides the example discussed above, another example is the not-actually-true headline we recently saw at this site, a headline of "Molecular Mechanisms Behind Learning and Memory Identified." The story refers to a scientific paper that merely dealt with aversive memory in mice. The paper reveals that the study (which failed to follow a blinding protocol) used way-too-small study group sizes such as groups of only 6 or 7 or 8 mice.  As a general rule of thumb, 15 subjects per study group is the minimum for a moderately convincing result.  It's the same old story that has been going on for decades in the field of experimental neuroscience: experimenters using way-too-small study groups, and getting what are probably only false alarms, with the experimenters wrongly proclaiming that some important discovery was made.  

The authors of the paper would have discovered how way-too-small  their study group sizes were if they had done what should be done by anyone doing an animal experiment: the performance of a sample size calculation, in which you estimate how large a study group size is needed in order for the study to have a good statistical power. The authors confess that they failed to perform such a calculation. They state this:

"The sample sizes were not pre-determined. For all molecular biology experiments, cellular biology experiments, and behavior tests, sample size was chosen according to previous studies."

Since the use of way-too-small study group sizes is currently an epidemic in experimental neuroscience, with most experimenters failing to use adequate sample sizes, you do not at all justify your choice of a sample size by saying "sample size was chosen according to previous studies." 

Another example of a recent unjustified headline on www.neurosciencenews.com is a headline of "Brain Region Found to Play a Crucial Role in Weighing Information From Different Sources." No such thing was actually discovered, because the scientific study mentioned was based on analysis of only two monkeys, a study group size way too small for a reliable result.