Male and female brain rhythms show differences


Male and female brains are different, functionally and anatomically. But that the EEG (electro-encephalography)
signals, measured using electrodes placed on the skull, show different
signals as well, was not demonstrated yet. For the interpretation of EEG
signals, advanced pattern recognition techniques have been developed
the last decades. Still, in many cases, the trained eye of the
neurologist gives better results. Even these trained eyes are not able
to recognize a difference between male and female brain rhythms. For
this, artificial intelligence, so-called ‘deep learning’, is needed.


researchers had a large set of over 1300 EEG patterns, from several
laboratories, at their disposal. This set has been entered into a
learning computer, a so-called ‘convolutional neural network’. This is
an artificial neural net existing of several layers and determining over
nine million parameters. The network was first trained using 1000
EEG’s of just two minutes, with a known outcome: male or female. This
was not a training based on entering specific characteristics, as these
were not known beforehand. After training, the computer was fed with an
independent set of EEGs. In over 80 percent, the system gives the right
answer. This is well above the significance threshold.


(Image caption: The multilayer neural net setup, used for classifying EEG readings)


The next step:
extract the specific features that make the difference, from the neural
net. The main difference is in the ‘beta activity’, a frequency range
between 20 and 25 Hz. These rhythms have to do with cognition and with
tasks that are emotionally positive or negative. It is known from
previous research that females are better capable of recognizing
emotion: this could indicate a difference in beta activity.  Within the
context of this research project, this has not been elaborated further.
The outcomes don’t give an answer to transgender issues either.


seems a complicated way of assessing sex, using EEG and strong
computing power. An interesting question, however, can be: do females
and males respond to neurological or psychiatric disorders in different
ways. And thus: is it, based on this knowledge, possible to better
tailor the treatment? Apart from that, this research shows that there is
a lot more information inside an EEG than meets the eye. Previous
research using the combination of EEGs and deep learning, was on sleep
analysis, responds to music or early detection of brain diseases. There
is enough reason for further exploring this potential, as it could lead
to better insights and personalized treatment.

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