Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG
This work addresses brain-computer interface development for patients with motor and visual disorders by providing a more accurate and robust method for EEG-based task classification, though it is incremental as it builds on existing deep learning approaches.
The authors tackled mental arithmetic task classification from EEG signals using a shallow convolutional neural network with spectral-temporal features, achieving a classification accuracy of 90.68% and improved robustness with only 3% standard deviation in cross-subject accuracy compared to 15.6% from conventional methods.
In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.