EEG-based communication: improved accuracy by response verification.

EEG-based communication: improved accuracy by response verification.
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DOI:
10.1109/86.712231
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发表时间:
1998-09-01
期刊:
IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子:
--
通讯作者:
Pfurtscheller, G
Pfurtscheller, G
中科院分区:
其他
文献类型:
--
作者:
Wolpaw, J R;Ramoser, H;Pfurtscheller, G

文献摘要

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人类可以学习控制感觉运动皮层特定频段的脑电图(EEG)活动幅度,并用它来将光标移动到计算机屏幕上的目标。基于脑电图的沟通可以为运动障碍人士提供新的增强沟通渠道。在本系统中,光标移动的每个维度都由线性方程控制。虽然方程中的截距不断更新,但它并不能完全消除脑电图振幅自发变化的影响。此缺陷降低了光标移动的准确性。我们评估了一种反应验证(RV)程序,其中每个结果由两项相反的试验(例如,一项顶部目标试验和一项底部目标试验)确定。两者的成功或失败都是确定结果的必要条件。 RV 程序减少了由于拦截选择不完善而导致的错误。相反试验对的准确度超过了根据单个试验的准确度预测的准确度,并且大大超过了相同试验对的准确度。当第一次试验有超过 2 个可能的目标时,RV 过程应该特别有价值,因为第二次试验只需要确认或否定第一次的结果,并且它应该适用于非线性和线性算法。
Humans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.