BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-22 DOI:10.1088/1741-2552/acf78a
Konstantinos Barmpas, Yannis Panagakis, Dimitrios A Adamos, Nikolaos Laskaris, Stefanos Zafeiriou
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Abstract

Objective.Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available.Approach.In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations.Main results.We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier.Significance.In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.

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脑电波散射网:一种用于基于脑电图的运动图像识别的轻量级网络。
脑机接口(BCI)通过脑电图(EEG)信号测量一个人的神经活动,实现大脑与外部世界的直接通信。近年来,卷积神经网络(CNNs)已被广泛用于在各种基于EEG的任务中进行自动特征提取和分类。然而,它们不可否认的好处被缺乏可解释性属性以及在只有有限数量的训练数据可用时无法充分执行所抵消。方法。在这项工作中,我们介绍了一种新的、轻量级的、完全可学习的神经网络架构,该架构依赖于Gabor滤波器将EEG信号信息沿频率和缓慢变化的时间调制离域到散射分解路径中。主要结果。我们在两种不同的建模设置中使用我们的网络,用于构建通用(跨主题训练)或个性化(在主题内训练)分类器。意义。在这两种情况下,使用两个不同的公开可用数据集和一个内部收集的数据集,与其他最先进的深度架构相比,我们展示了我们的模型的高性能,可训练参数的数量要少得多,训练时间也更短。此外,我们的网络展示了在时间滤波操作层面出现的增强的可解释性特性,并使我们能够用有限的训练数据训练高效的个性化脑机接口模型。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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