Brain Computer Interface Based Thought Recognition System Using a Hybrid Deep Learning Model

Brain-Computer Interface (BCI) connects the human brain with computers and electronic devices. The signals from human brain are processed using several deep-learning techniques to convert them into a comprehensible form. Among these techniques, the convolutional neural network (CNN) model has excellent performance in BCI recognition. However, the existing CNN model is prone to over-fitting and has limitations with accuracy. The model complexity must be increased to achieve better accuracy. To address these concerns, a novel hybrid R-CNN model for BCI thought recognition is proposed in this work. The convolution layer of CNN and the Long short-term memory (LSTM) layer of recurrent neural network (RNN) is utilized for this purpose. A batch normalization layer is also instigated to reduce over-fitting. Further, a rectified linear unit (ReLU) is engaged to speed up training under as low as five epochs, along with a custom optimizer which optimizes some default values within the optimizer. Experiments are performed with BCI datasets of two different file sizes with different records. The first dataset size is 6.5 MB having 60684 records with three classes, and the second dataset size is 10.1 MB having 94119 records with five classes. Consequently, the proposed hybrid model exhibits a higher average accuracy of 95% for 6.5 MB file size and 98% for 10.1 MB file size, which is superior compared to the accuracy of existing deep learning models. Furthermore, the efficiency of the proposed novel hybrid R-CNN model is evaluated with some other performance measures such as F1-score, recall and precision.