Department of Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar Garhwal, 246174, Uttarakhand, India.
Department of Neurology, All India Institute of Medical Science Bibinagar(Hyderabad Metropolitan Region), Bibinagar, 508126, Telangana, India.
Phys Eng Sci Med. 2024 Sep;47(3):939-954. doi: 10.1007/s13246-024-01417-w. Epub 2024 Apr 22.
Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers. However, more research needs to be conducted on how subject-specific characteristics such as mental state, age, handedness, nativeness and resting state activity affects the brain's output during imagined speech. In an overt speech, it is evident that native and non-native speakers' brains function differently. Therefore, this paper explores how nativeness to language affects EEG signals while imagining vowel phonemes, using brain-map analysis and scalogram and also investigates the inclusion of features extracted from resting state EEG with imagined state EEG. The Fourteen-channel EEG for Imagined Speech (FEIS) dataset was used to analyse the EEG signals recorded while imagining vowel phonemes for 16 subjects (nine native English and seven non-native Chinese). For the classification of vowel phonemes, different connectivity measures such as covariance, coherence, and Phase Synchronous Index-PSI were extracted and analysed using statistics based Multivariate Analysis of Variance (MANOVA) approach. Different fusion strategies (difference, concatenation, Common Spatial Pattern-CSP and Canonical Correlation Analysis-CCA) were carried out to incorporate resting state EEG connectivity measures with imagined state connectivity measures for enhancing the accuracy of imagined vowel phoneme recognition. Simulation results revealed that concatenating imagined state and rest state covariance and PSI features provided the maximum accuracy of 92.78% for native speakers and 94.07% for non-native speakers.
沟通对于残疾个体来说具有挑战性，但随着脑机接口（BCI）系统的发展，可以开发出替代的通信系统。当前的 BCI 拼写器，如 P300、SSVEP 和 MI，存在依赖外部刺激或与对话无关的心理任务等缺点。相比之下，基于想象语音的 BCI 系统依赖于直接解码用户正在思考的元音/单词，使其更加直观、用户友好，并且在脑机接口（BCI）研究人员中非常受欢迎。然而，需要进一步研究特定于主题的特征（如心理状态、年龄、惯用手、母语和静息状态活动）如何影响想象语音时大脑的输出。在显性语音中，母语和非母语使用者的大脑功能不同是显而易见的。因此，本文探讨了母语对语言的影响如何影响想象元音音素时的 EEG 信号，使用脑图分析和谱图，并研究了从静息态 EEG 中提取的特征与想象态 EEG 的融合。使用十四通道想象语音脑电图（FEIS）数据集来分析 16 名被试（9 名母语为英语，7 名母语为中文）在想象元音音素时记录的 EEG 信号。对于元音音素的分类，提取了协方差、相干性和相位同步指数-PSI 等不同的连通性度量，并使用基于统计的多元方差分析（MANOVA）方法进行了分析。采用不同的融合策略（差异、串联、共同空间模式-CSP 和典型相关分析-CCA），将静息态 EEG 连通性度量与想象态连通性度量相结合，以提高想象元音音素识别的准确性。仿真结果表明，串联想象态和静息态协方差和 PSI 特征可分别为母语使用者提供 92.78%的最大准确性和非母语使用者 94.07%的最大准确性。
