Abstract
Electroencephalography (EEG)-based brain梒omputer interface (BCI) is a non-invasive technology with potential in various healthcare applications including stroke rehabilitation and neuro-feedback training. These applications typically require multi-channel EEG. However setting up a multi-channel EEG headset is time-consuming potentially resulting in patient reluctance to use the system despite its potential benefits. Therefore we investigated the appropriate number of electrodes required for a successful BCI application in wearable devices using various numbers of EEG channels. EEG multi-frequency features were extracted using the 揻ilter bank� feature extraction technique. A support vector machine (SVM) was used to classify a left/right-hand opening/closing motor imagery (MI) task. Nine electrodes around the center of the scalp (F3 Fz F4 C3 Cz C4 P3 Pz and P4) provided high classification accuracy with a moderate setup time; hence this system was selected as the minimal number of required channels. Spherical spline interpolation (SSI) was also applied to investigate the feasibility of generating EEG signals from limited channels on an EEG headset. We found classification accuracies of interpolated groups only and combined interpolated and collected groups were significantly lower than the measured groups. The results indicate that SSI may not provide additional EEG data to improve classification accuracy of the collected minimal channels. The conclusion is that other techniques could be explored or a sufficient number of EEG channels must be collected without relying on generated data. Our proposed method which uses a filter bank feature session-dependent training and the exploration of many groups of EEG channels offers the possibility of developing a successful BCI application using minimal channels on an EEG device. ? 2024 by the authors.
