Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces
Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces
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DOI：
10.1109/tnsre.2010.2053387
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发表时间：
2010-08-01
影响因子：
4.9
通讯作者：
Millan, Jose del R.
中科院分区：
文献类型：
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作者：
Chavarriaga, Ricardo;Millan, Jose del R.
我们描述了错误相关的电位，而人类用户的外部代理的性能进行监控，并讨论其使用一种新型的脑机交互。在这种方法中，错误相关的脑电图（EEG）电位的单次试验检测被用来推断最佳代理行为，通过降低代理决策的概率，引起这样的潜力。与传统方法相比，用户充当外部自治系统的评论家，而不是不断生成控制命令。这设置了一个认知监控回路，其中人类直接提供有关整体系统性能的信息，这些信息反过来可以用于其改进。我们表明，从EEG中识别错误和正确的代理决策是可能的（平均识别率分别为75.8%和63.2%），并且引发的信号在很长一段时间内（从50到> 600天）是稳定的。此外，这些性能允许在几次试验后推断出脑机交互范例中简单代理的最佳行为。
We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of brain-computer interaction. In this approach, single trial detection of error-related electroencephalography (EEG) potentials is used to infer the optimal agent behavior by decreasing the probability of agent decisions that elicited such potentials. Contrasting with traditional approaches, the user acts as a critic of an external autonomous system instead of continuously generating control commands. This sets a cognitive monitoring loop where the human directly provides information about the overall system performance that, in turn, can be used for its improvement. We show that it is possible to recognize erroneous and correct agent decisions from EEG (average recognition rates of 75.8% and 63.2%, respectively), and that the elicited signals are stable over long periods of time (from 50 to > 600 days). Moreover, these performances allow to infer the optimal behavior of a simple agent in a brain-computer interaction paradigm after a few trials.