Color-based classification of EEG Signals for people with the severe locomotive disorder
This work provides an alternative input method for individuals with severe locomotive disorders, though it appears incremental as it applies existing attention-based LSTM methods to a specific domain.
The paper tackled the problem of classifying EEG signals for people with severe locomotive disorders by mapping colors to functions like directional movement, achieving an accuracy of 93.5% for two colors and 65.75% for four colors using an attention-based LSTM network.
The neurons in the brain produces electric signals and a collective firing of these electric signals gives rise to brainwaves. These brainwave signals are captured using EEG (Electroencephalogram) devices as micro voltages. These sequence of signals captured by EEG sensors have embedded features in them that can be used for classification. The signals can be used as an alternative input for people suffering from severe locomotive disorder.Classification of different colors can be mapped for many functions like directional movement. In this paper, raw EEG signals from NeuroSky Mindwave headset (a single electrode EEG sensor) have been classified with an attention based Deep Learning Network. Attention based LSTM Networks have been implemented for classification of two different colors and four different colors. An accuracy of 93.5\% was obtained for classification of two colors and an accuracy of 65.75\% was obtained for classifcation of four signals using the mentioned attention based LSTM network.