Model-based responses and features in Brain Computer Interfaces.
Model-based responses and features in Brain Computer Interfaces.
复制标题
脑机接口中基于模型的响应和特征。
DOI：
10.1109/iembs.2008.4650208
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Gluckman,BruceJ
中科院分区：
文献类型：
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作者：
Kamrunnahar,M;Dias,NS;Schiff,SJ;Gluckman,BruceJ
为了发展脑-机接口（BCI），将新的基于模型的特征引入到基于头皮脑电（EEG）的运动想象任务识别中。我们已经获得了人类头皮脑电开环和反馈条件下，以响应基于线索的运动想象任务。EEG信号，转换成频率特定的频带，如μ，β和运动相关电位，用于特征提取，目的是区分任务。使用功率谱和基于模型的参数等特征对数据进行分类。两种不同的特征选择方法：逐步和主成分分析（PCA），结合线性判别分析（LDA）。不同的训练/验证标准被应用于任务相关特征的分类。结果表明，开环条件下的手/趾/舌运动和反馈条件下的左/右手运动的表象任务的头皮脑电相关性可以很好地区分，分类误差小于20%。基于模型的技术，这导致在2%-30%的范围内的分类误差，有可能使用先进的控制系统理论在BCI的发展，以实现改进的性能相比，目前应用的比例控制或滤波算法实现的性能。
Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%–30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.