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Working memory (WM) is a mechanism that temporarily stores and manipulatesinformation in service of behavioral goals and is a highly dynamic process.Previous studies have considered decoding WM load using EEG but have notinvestigated the contribution of sequential information contained in thetemporal patterns of the EEG data that can differentiate different WM loads. Inour study, we develop a novel method of investigating the role of sequentialinformation in the manipulation and storage of verbal information at varioustime scales and localize topographically the sources of the sequentialinformation based decodability. High density EEG (128-channel) were recordedfrom twenty subjects performing a Sternberg verbal WM task with varying memoryloads. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) were trainedto decode memory load during encoding, retention, activity-silent, andretrieval periods. Decoding accuracy was compared between ordered data and atemporally shuffled version that retains pattern based information of the databut not temporal relation to assess the contribution of sequential informationto decoding memory load. The results show that (1) decoding accuracy increaseswith increase in the length of the EEG time series given to the LSTM for bothordered and temporally shuffled cases, with the increase being faster forordered than temporally shuffled time series, and (2) according to the decodingweight maps, the frontal, temporal and some parietal areas are an importantsource of sequential information based decodability. This study, to ourknowledge, is the first study applying a LSTM-RNN approach to investigatetemporal dynamics in human EEG data in encoding WM load information.

Decoding Working Memory Load from EEG with LSTM Networks