可解释性
计算机科学
卷积神经网络
解码方法
人工智能
自闭症
贝叶斯优化
贝叶斯概率
自闭症谱系障碍
模式识别(心理学)
集合(抽象数据类型)
机器学习
算法
心理学
发展心理学
程序设计语言
作者
Davide Borra,Elisa Magosso,Miguel Castelo-Branco,Marco Simões
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2022-07-14
卷期号:19 (4): 046010-046010
被引量:5
标识
DOI:10.1088/1741-2552/ac7908
摘要
Objective.P300 can be analyzed in autism spectrum disorder (ASD) to derive biomarkers and can be decoded in brain-computer interfaces to reinforce ASD impaired skills. Convolutional neural networks (CNNs) have been proposed for P300 decoding, outperforming traditional algorithms but they (a) do not investigate optimal designs in different training conditions; (b) lack in interpretability. To overcome these limitations, an interpretable CNN (ICNN), that we recently proposed for motor decoding, has been modified and adopted here, with its optimal design searched via Bayesian optimization.Approach.The ICNN provides a straightforward interpretation of spectral and spatial features learned to decode P300. The Bayesian-optimized (BO) ICNN design was investigated separately for different training strategies (within-subject, within-session, and cross-subject) and BO models were used for the subsequent analyses. Specifically, transfer learning (TL) potentialities were investigated by assessing how pretrained cross-subject BO models performed on a new subject vs. random-initialized models. Furthermore, within-subject BO-derived models were combined with an explanation technique (ICNN + ET) to analyze P300 spectral and spatial features.Main results.The ICNN resulted comparable or even outperformed existing CNNs, at the same time being lighter. BO ICNN designs differed depending on the training strategy, needing more capacity as the training set variability increased. Furthermore, TL provided higher performance than networks trained from scratch. The ICNN + ET analysis suggested the frequency range [2, 5.8] Hz as the most relevant, and spatial features showed a right-hemispheric parietal asymmetry. The ICNN + ET-derived features, but not ERP-derived features, resulted significantly and highly correlated to autism diagnostic observation schedule clinical scores.Significance.This study substantiates the idea that a CNN can be designed both accurate and interpretable for P300 decoding, with an optimized design depending on the training condition. The novel ICNN-based analysis tool was able to better capture ASD neural signatures than traditional event-related potential analysis, possibly paving the way for identifying novel biomarkers.
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