Detecting P300-ERPs Building a Post-validation Neural Ensemble with Informative Neurons from a Recurrent Neural Network

We introduce a novel approach for detecting the sample-level temporal structure of P300 event-related potentials. It consists of extracting the most informative neurons from a Recurrent Neural Network and building a post-validation neural ensemble (PVNE). The weights connecting the recurrent and the output layers are used to rank the recurrent neurons according to their relevance when generating the network’s output. A set of neurons is selected according to their positions in this ranking, and their individual predictions are then combined to obtain the final model’s output. This procedure discards neurons whose role could be more related to maintaining the network’s hidden state than to detecting the P300 events, with an overall performance increase. The use of L1 regularization notably emphasizes this effect. We compare the performance of this approach with both Elman and LSTM RNNs and show that the PVNE is able to detect the sample-level temporal structure of P300 event-related potentials, outperforming the standard models. Sample-level prediction also allows for real-time monitoring of the EEG signal generation related to ERPs.