Solving the Memory-based Memoryless Trade-off Problem for EEG Signal Classification

Abstract

Electroencephalogram (EEG) signals exhibit highly irregular patterns. This irregularity, which arises from i.i.d. measurement noise, has been partially resolved by memoryless classifiers, such as deep convolutional neural networks (CNN). However, there are other major sources of irregularity, including brain network modes, mental states, and various physiological factors. These internal states drift over time, in which case it would be better to use memory-based neural networks, such as long short-term memory networks (LSTM). This paper presents a novel EEG signal classification framework that resolves a trade-off between memoryless and memory-based classification. The proposed method uses deep reinforcement learning (RL) to find a trial-by-trial control strategy for the attention control system that switches between CNN (memoryless) and LSTM (memory-based)-or is a mixture of both. The simulation on the EEG dataset, which was collected while performing a complex cognitive task, shows that the proposed attention control system outperforms other EEG classification methods.