What's New in Neurofeedback

• The way of the brainwave - Natl Post, www.nationalpost.com/search/story.html?f=/stories/20010405/523878.html
  

  Cap stops drivers falling asleep - www.abc.net.au/science/news/health/HealthRepublish_307679.htm

  Brain signals to capture audience reaction - www.philly-tech.com/archives/2001/04/mindreaders.asp

  NOW IN PAPERBACK!
  A Symphony in the Brain: The Evolution of the New Brain Wave Biofeedback
  by Jim Robbins
   Development of neurofeedback from its discovery by a small corps of research scientists to its growing application across the country and its present battle for acceptance in the medical & psychological community.  - www.amazon.com/exec/obidos/ASIN/0802138195/eegspectrum/

New Year's Eve, 1984. New York City. Three hours before midnight. Thirty feet underground. The subway car squealed slowly to a stop at the 96th Street Station. As the side doors opened to receive passengers, the air erupted with sound. Someone was shouting on the platform, or perhaps they were singing. The same few sounds over and over. Whoever it was, he was loud enough to attract my attention. I glanced up from the book I was reading, but there was nothing out of the ordinary from what I could see. Passengers seated here and there, waiting patiently for the subway to move on. From my angle it was hard to see what the disturbance was on the platform. A few seconds passed and the doors shut with a bang. The subway pulled away and drew into the tunnel, the interior lights flickering on and off. Even though I could still hear the shouting, I thought nothing more about the disturbance, expecting it would all fade away as we picked up speed. Instead the shouts grew ever louder.

Nine days before, Bernard Goetz had shot four teenagers in this very subway.

I could hear chanting clearly now. Two or three cars away, and coming this way. My sole escape route, if I needed one, would be through the flimsy sliding door behind me. It connected to the next car -- not much of an escape.

The far door crashed open and in they marched, in they stormed. Jackets and caps, flailing arms, pounding fists, angry faces. A twisting hydra of fury and rebellion, spitting and screaming at every person they passed:

Here I sat alone, in an empty corner of the train, white, male, college-educated -- easily mistaken for the "power that be." Albeit, in larval form. ... but before I complete my (true) account, I'd like to explain the reason why I was reminded of this night long since past. Australian psychologists recently published a report on QEEG differences in children based on sex and age. EEG data was acquired from forty boys and forty girls, age 8 to 12 years old. It ought to be a remarkable paper, well worth citing, but because they applied power transforms to the spectral estimates, the paper was nearly useless to me.

Power transforms are a vestige of our clinical history. This distribution-distorting technique, entirely unsuited for statistical inquiries, served the initial investigators of brain waves well, when rulers and VAX machines were used to make sense of squiggly lines. Squaring spectral means emphasizes extreme data points in a sample. It launches fringe events far away from the central tendency of a sample's distribution so that they can be easily identified and circled with red ink by the psychologist on duty. It is a technique to assist eyeballs, not computers. It is a geometric transformation that should not be embraced in the linear world of inferential statistics, but people continue to leave the welcome mat out.

Although power transformations and power distributions are essential functions in engineering and other sciences (perhaps even including psychology), they should be applied sparingly and after careful consideration of a data's sampled distribution. QEEG data, if anything, should be geometrically compressed or compacted in order to make it correspond readily with psychological phenomena. Power geometrically expands it.

Let's take a fresh look at what we are doing when we convert magnitude values to power. Here are some examples:

• The Los Angeles Lakers won 16 squared games out of a best of 49 squared games series to win the NBA Championship. Thank goodness. Had it gone to Game 36 squared, Philly might have been able to pull it out.
• I was doing 1600 sq mph in a 1225 squared mph speed zone, a mere 25 squared mph above the limit, correct?
• It was sweltering out today, 7744 squared degrees Fahrenheit, 962 squared degrees Celsius.
• Premium gasoline costs $$ 4.45 a gallon this summer but I remember when it was only $$ 1.64. My father loved to talk about how it was a mere $$ 0.02 in his youth.

Comparing squared values makes little sense in most endeavors. Not so far off in QEEG. Let's look at what power transforms do to the simplest case, the mean of two values:

Always keep close to the raw. It's a good rule of thumb in measurement; the farther one gets away from actual measurements, the more likely error has crept in. This adage is doubly appropriate when statistics are to be used, because statistics, as we all know, are the tools of the devil, a formalized scheme to confound and to confuse and occasionally illuminate.

Power is a geometric function; averaging is linear. When the two are combined, as is commonly done in QEEG analysis (mean power for a task or subject), results vary depending upon when and where each step was performed. QEEG analysis includes a whole lot of averaging along the way. It's not always clear which average was squared to produce power values. As epoch size varies, so does results, unfortunately.

32 seconds of alpha activity from site Pz, one subject. Data was analyzed using four different epoch lengths:

Epoch Length	Magnitude	Power
1 s	2.12	5.18
2 s	2.12	4.78
4 s	2.12	4.57
32 s	2.12	4.49

Notice how mean power varies in response to epoch duration. This is not good. Only when the epoch encompasses the entire dataset is there no difference between magnitude and power (4.49 is 2.12 squared). I don't know about you, but I don't like to see the EEG changing once the subject has gone home.

Finally, let's show why it's not good for your analysis. Which figure of alpha activity during eyes closed baselines (n=275) looks more normally distributed, more like a bell-shaped curve?

During active tasks, QEEG values are generally attenuated, which reduces the skewing, but despite this reduction, power transforms produce highly skewed distributions (Dmax= 0.17, p <.01; cf. Kolmogorov, 1941) whereas magnitudes are only moderately skewed (Dmax= 0.12, p <.05) (n=20).

Little more can I do to show the distortion inherent in power values, so I guess I can return to my tale.

When the mob descended on my lonely seat, I acted like any New Yorker would: I ignored them, pretended that this was a normal everyday occurrence (what did I know; perhaps it actually was). I never looked up from my book. The chanting men passed me by and moved onto the next car. Perhaps they realized that anyone riding the subway was unlikely to hold any power. Perhaps I had been perfectly safe all along. Nevertheless, after the ball dropped and the throng of thousands sang a drunken round of Auld Lang Syne in Times Square, I conjured up some of that elusive power, pulled out a twenty, skipped the subway entrance, and hailed a taxicab home.

DK

Related reading:

Clarke AR, Barry RJ, McCarthy R, Selikowitz M. (2001). Age and sex effects in the EEG: development of the normal child. Clinical Neurophysiology, 112, 806-14. www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?form=6&db=m&uid=11336896

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