Makeig S, Enghoff S, Jung T P, Sejnowski T J
Naval Health Research Center, San Diego, CA 92186, USA.
IEEE Trans Rehabil Eng. 2000 Jun;8(2):208-11. doi: 10.1109/86.847818.
The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.
无创脑控电脑屏幕显示或移动设备的前景引起了许多人的兴趣，对于一些“闭锁综合征”患者来说至关重要，这些患者虽然保留了感觉和智力，但几乎完全丧失了运动能力。目前，有几个研究小组正试图通过对自发头皮脑电图（EEG）的特定特征进行操作性条件反射来实现对屏幕显示的脑控，这些特征包括中央μ节律（9-12赫兹）。一种新的脑电图分解技术——独立成分分析（ICA），似乎为设计用于检测和操作性控制内源性脑电节律以实现灵活的基于脑电图的通信系统的新研究奠定了基础。ICA将多通道脑电图数据分离为空间上静态且时间上独立的成分，包括分别对应后部α节律和中央μ活动的成分。我们利用视觉选择性注意任务的数据证明，与单头皮通道的活动测量相比，ICA衍生的μ成分对运动事件的频谱反应性要强得多。因此，自发脑电图的ICA分解似乎为操作性条件反射奠定了自然基础，以便在运动受限和闭锁综合征患者中实现高效和多维的脑控。
