Deep learning the EEG manifold for phonological categorization from active thoughts
Table 2: Results in accuracy on 10% test data in the first study
['[BOLD] Method', '±  [BOLD] Bilab', '±  [BOLD] Nasal', '[BOLD] C/V', '±  [BOLD] /uw/', '±  [BOLD] /iy/']
As such, we concatenate the last fully-connected layer from the CNN with its counterpart in the LSTM to compose a single feature vector based on these two penultimate layers. Ultimately, this forms a joint spatio-temporal encoding of the cross-covariance matrix. A detailed analysis on repeated runs further shows that in most of the cases, LSTM alone does not perform better than chance. CNN, on the other hand, is heavily biased towards the class label which sees more training data corresponding to it. Though the situation improves with combined CNN-LSTM, our analysis clearly shows the necessity of a better encoding scheme to utilize the combined features rather than mere concatenation of the penultimate features of both networks.