Electroencephalogram (EEG) is the signal emitted by our brain as it’s working. This signal comes from our neural activity, which can be detected on the surface of the skull. Even though technology today allows for measuring EEG, it is not yet possible to decrypt the initial message sent by the nerve cells.
The science behind our technology
We could compare EEG to a microphone placed outside of a football stadium. We hear people cheer and we know that a goal has been scored. However, we would not be able to know which team it is, what the score is or which player scored the goal to begin with.
Measurement of various states
EEG is a way to get an overview of what happens in the brain. To put it simply, activities, or states, are classified according to their oscillation frequency range. As we can see in this experiment conducted with our “N” buds, oscillations between 7 and 13 Hz, called alpha waves, can be observed when the subject is in a calm, resting or relaxed state.
Other types of waves appear at various states:
Delta waves, 0,5 to 4 Hz (deep sleep);
Theta waves, 4 to 7 Hz (nap or meditation);
Beta waves, 13 to 30 Hz (active and alert state);
Gamma waves, 30 to 100 Hz (intense neuronal activity).
Anomalies and neurological disorders
EEG is a powerful tool to detect anomalies associated with different types of pathology in the brain activity. Consequently, it is widely used to diagnose epilepsy, the second most common chronic neurological disorder in the world.
In this experiment, the “N” buds detected a patient’s epileptic seizure in a real-life situation. The seizure is easily noticeable on the graph in contrast with the patient’s state before and after the episode.
EEG is a very low amplitude signal, which makes it vulnerable to parasites like the heart’s electrical activity as well as eye, face and body movements.
These parasites are called artefacts. Artefacts disrupt the recording of the brain activity and can even suppress the signal of interest. Moreover, they can be misinterpreted for neurological anomalies and therefore make the use of EEG outside of clinical situations challenging.
Luckily, our algorithms are rapidly improving in detecting these artefacts and separating them from EEG.