The challenges of controlling robots using our thoughts

Noise in the nervous system
The EEG signals are very noisy. The information of interest is normally mixed up with several irrelevant but concurrent neural activities called central nervous system (CNS) noise.CNS noise may interfere with the information of interest due to overlapped characteristics. For example, BCI systems that work based on imagination of body movement involve the brain patterns in the μ-rhythm (i.e. 8-13Hz). However, the α-rhythm, which represents the visual and mental efforts, has the same frequency range as the μ-rhythm. Although the α-rhythm originates from the occipital lobe of the brain, due to the volume conduction, it interferes with the μ-rhythm that originates from the motor cortex.Besides, the α-rhythm is quite prominent, whereas the μ-rhythm is weaker and can only be observed after appropriate signal processing.Non-stationarity
Another problem related to classification of EEG signals is non-stationarity. Variation in the EEG properties, called non-stationarity, can result in deteriorated BCI performances as most of the machine learning algorithms implicitly assume stationary data.This means if the model is fitted to the subject’s training data, it may not be optimal for a new session (recorded on another day) or even new trials in the same session. The plausible reasons for the changes in the dynamic of the EEG signals are as follows:- The physical properties of the EEG electrodes change over time. For example, the impedance of the electrodes can change due to drying up of the conductive gel, or the position of the electrodes can change when the EEG cap is reused in a new session.
- Neurophysiological conditions (e.g. fatigue) can be variable over time.
- Psychological parameters, such as motivation, attention and task involvement also display a large variability.
- Artifacts caused by body movements or muscular activities such as swallowing or blinking change the signal properties.
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