Design of EEG Based Wheel Chair by Using Color Stimuli and Rhythm Analysis
Design of EEG Based Wheel Chair by Using Color Stimuli and Rhythm Analysis
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利用颜色刺激和节律分析设计基于脑电图的轮椅
DOI：
10.1109/icasert.2019.8934493
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Md. Akramul Alim
中科院分区：
文献类型：
--
作者：
Nafiul Hasan;Md Mahmudul Hasan;Md. Akramul Alim
提出了一种基于颜色刺激的脑控智能轮椅设计方法。一个一般的方法，以找出最有效的节奏的颜色分类。原色RGB和二次色黄色被选择用于左、右、前进和停止命令。分析了三个不同受试者的α、β、θ、δ节律。利用时、频域的不同特征，建立了12个人工神经网络来确定最佳节奏。对每个脑电信号进行主成分分析，以消除颜色刺激的背景效应。比较结果表明，在所有设计的人工神经网络中，β节律是最有效的节律，其最小均方误差为4.845×10-9。
A novel methodology of brain controlled intelligent wheelchair by using color stimuli is proposed here. A general methodology is applied to find out most effective rhythm for color classification. Primary colors RGB and secondary color yellow were chosen for left, right, forward and stop command. Alpha, Beta, Theta, Delta rhythms were analyzed for three different subjects. Using dissimilar features of time and frequency domain twelve artificial neural network were built to decide the best rhythm. Principal component analysis was made for each EEG signal to remove the background effect of color stimuli. Comparing the findings it is visualized that beta rhythm is the most efficient rhythm with minimum mean square error of 4.845×10-9 among all designed ANN for color classification.