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Diagnostic biomarker discovery from brain EEG data with LSTM, reservoir-SNN and NeuCube: Methods and a pilot study on epilepsy vs migraine
  • Nikola Kasabov
Nikola Kasabov
Auckland University of Technology

Corresponding Author:[email protected]

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Abstract

The paper explores how deep LSTM and deep spiking neural networks (SNN) can be used to extract meaningful features from spatio-temporal EEG brain data for early, on-line diagnosis. It introduces a new online spike encoding algorithm for Izhikevich neural networks and new methods for learning and diagnostic biomarker discovery for each of the three most popular deep learning neural network models, namely: deep BiLSTM; reservoir SNN; and NeuCube. The methods reveal that hidden neurons in a BiLSTM can capture biological meaning, while a reservoir SNN neuron activities and the spiking activities in a NeuCube model can be used to discover EEG channels that can be utilized as brain biomarkers. The study used EEG data from three datasets related to epileptic-, migraine- and healthy subjects. The problem of discriminating epilepsy from migraine using EEG data is a well known hard problem.  LSTM and a reservoir SNN achieved 90% and 85% classification accuracy correspondingly and indicated that channels F8, T3,T’6 and F7,T6 correspondingly can be explored as potential biomarkers, while a NeuCube model achieved 97% with T6, F7 and also C4 and F8 as potential biomarkers for a better classification. In addition, a 2 class NeuCube model discriminated perfectly well epilepsy from migraine and pointing to two channels C4 and F8 as potential biomarkers for the task. While all models resulted in a good classification and feature selected, the main reason for the superior performance of NeuCube is its brain-inspired architecture, where a brain map is used to structure a 3D SNN model to capture deep spatio-temporal dynamics from EEG data. This research has important implications for the development of more effective algorithms for on-line EEG classification, analysis and early brain state diagnosis, adding new features of explainability and discovery to otherwise effective AI models. The proposed methods and findings can be used for the development of more efficient brain-computer interfaces and for clinical practice.