A Self-Supervised Task-Agnostic Embedding for EEG Signals

A. Partovi, A. Burkitt, D. Grayden
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Abstract

Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. The success of a BCI system is largely driven by the accuracy of the BCI decoder. This accuracy, in turn, may be limited by the amount of labelled training data available for supervised machine learning algorithms. The success of deep learning algorithms in other computer science areas has not reached the field of BCI decoding due to this lack of abundant labelled data. We use a novel deep learning architecture trained in a self-supervised manner to learn a common vector representation (embedding) of EEG signals that can be used in different BCI tasks. The vector representation is trained using EEG recordings without using any task labels. We validate our embedder using two separate BCI tasks: seizure detection and motor imagery, and assess its usefulness through distance similarity metrics in a clustering approach. The derived embeddings were successful in distinguishing binary classes in both tasks.
脑电信号的自监督任务不可知嵌入
脑机接口(bci)在改善残疾人生活方面具有巨大潜力。BCI系统的成功很大程度上取决于BCI解码器的准确性。反过来,这种准确性可能会受到有监督机器学习算法可用的标记训练数据数量的限制。由于缺乏丰富的标记数据,深度学习算法在其他计算机科学领域的成功还没有达到脑机接口解码领域。我们使用一种以自监督方式训练的新颖深度学习架构来学习可用于不同脑机接口任务的EEG信号的公共向量表示(嵌入)。在不使用任何任务标签的情况下,使用EEG记录训练向量表示。我们使用两个单独的BCI任务来验证我们的嵌入器:癫痫检测和运动图像,并通过聚类方法中的距离相似度量来评估其有效性。派生的嵌入在两个任务中都成功地区分了二进制类。
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