AM-MTEEG: Multi-task EEG classification based on impulsive associative memory

TL;DR

A multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification, which improves average accuracy over state-of-the-art methods and reduces performance variance across subjects.

Abstract

Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-individual variability. Inspired by the principles of learning and memory in the human hippocampus, we propose a multi-task (MT) classification model, called AM-MTEEG, which combines learning-based impulsive neural representations with bidirectional associative memory (AM) for cross-individual BCI classification tasks. The model treats the EEG classification of each individual as an independent task and facilitates feature sharing across individuals. Our model consists of an impulsive neural population coupled with a convolutional encoder-decoder to extract shared features and a bidirectional associative memory matrix to map features to class. Experimental results in two BCI competition datasets show that our model improves average accuracy compared to state-of-the-art models and reduces performance variance across individuals, and the waveforms reconstructed by the bidirectional associative memory provide interpretability for the model's classification results. The neuronal firing patterns in our model are highly coordinated, similarly to the neural coding of hippocampal neurons, indicating that our model has biological similarities.

Figures (5)

• Figure 1: The overall architecture of the associative memory multi-task EEG (AM-MTEEG) model: Included are a convolutional encoder, impulsive neural population, a bidirectional associative memory (BAM) module, and a decoder with transposed convolution. Both the convolution layer and the transposed convolution layer use a convolution kernel of length 5 and are activated using the Relu function. In the experiment, we used 200 Leaky Integrate-and-Fire neurons as the impulsive neuron population. The encoder, impulsive neural population and decoder are trained by backpropagation (BP) with joint loss, and the BAM module is trained by Hebbian learning.
• Figure 2: The step function, impulse function, and surrogate gradient functions
• Figure 3: Ablation experiments on the BCI Competition III Iva binary classification dataset. (a) Original model (b) Model with spiking neurons removed (c) Model with a fully connected network using gradient descent instead of the associative memory classifier. The results show that the spiking neurons and bidirectional associative memory networks included in the model can improve classification performance.
• Figure 4: (a) Reconstructed waveform and (b) Event-related potential of each movement in BCI Competition IV IIa
• Figure 5: The time of neuronal spikes associated with two types of movement