Functional link PSO neural network based classification of EEG mental task signals

C. Hema, M. Paulraj, Sazali Yaacob, A. H. Adom, R. Nagarajan
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引用次数: 9

Abstract

Classification of EEG mental task signals is a technique in the design of Brain machine interface [BMI]. A BMI can provide a digital channel for communication in the absence of the biological channels and are used to rehabilitate patients with neurodegenerative diseases, a condition in which all motor movements are impaired including speech leaving the patients totally locked-in. BMI are designed using the electrical activity of the brain detected by scalp EEG electrodes. In this paper five different mental tasks from two subjects were studied, combinations of two tasks are used in the classification process. A novel functional link neural network trained by a PSO algorithm is proposed for classification of the EEG signals. Principal component analysis features are used in the training and testing of the neural network. The average classification accuracies were observed to vary from 80.25% to 93% for the 10 different task combinations for each of the subjects. The proposed network has an average training time of 0.16 sec. The results obtained validate the performance of the proposed algorithm for mental task classification.
基于功能链接PSO神经网络的脑电心理任务信号分类
脑电心理任务信号分类是脑机接口设计中的一项技术。BMI可以在没有生物通道的情况下为交流提供数字通道,并用于神经退行性疾病患者的康复。在这种疾病中,包括语言在内的所有运动都受损,使患者完全被锁定。BMI是利用头皮脑电图电极检测到的大脑电活动来设计的。本文研究了来自两名受试者的五种不同的心理任务,在分类过程中采用了两种任务的组合。提出了一种基于粒子群算法训练的功能链接神经网络,用于脑电信号的分类。主成分分析特征用于神经网络的训练和测试。在10种不同的任务组合中,平均分类准确率从80.25%到93%不等。该网络的平均训练时间为0.16秒。实验结果验证了该算法对心理任务分类的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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