Department of Neuroscience, University of Rome Tor Vergata, Via Montpellier 1, 00135, Rome, Italy.
Brain Topogr. 2010 Jun;23(2):180-5. doi: 10.1007/s10548-010-0143-0. Epub 2010 Apr 20.
We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain-computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (<400 ms) trial length traditionally used in this class of BCIs. To investigate which scalp regions conveyed information suitable for BCI, a stepwise linear discriminant analysis classifier was used. The method iteratively analyzed each individual sensor and determined its performance indicators. These were then plotted on a 2-D topographic head map. Preliminary results for both EEG and MEG data suggest that components other than the P300 maximally represented in the occipital region, could be successfully used to improve classification accuracy and finally drive this class of BCIs.
我们研究了在刺激呈现后最初的 800 毫秒内出现的哪种诱发反应成分最适合用于基于经典 P300 的脑机接口拼写器协议。数据是从两个对象的 275 个脑磁图传感器和四个对象的 61 个脑电图传感器中采集的。为了更好地描述诱发的生理反应并最大程度地减少反应重叠的影响，我们更喜欢使用 1000 毫秒的刺激间间隔，而不是传统上在这种 BCI 类中使用的短（<400 毫秒）试验长度。为了研究哪些头皮区域传递适合 BCI 的信息，我们使用了逐步线性判别分析分类器。该方法迭代地分析每个单独的传感器，并确定其性能指标。然后将它们绘制在二维头地形图上。EEG 和 MEG 数据的初步结果表明，除了在枕区中最大程度表示的 P300 之外的其他成分也可以成功用于提高分类准确性，并最终驱动这种 BCI。
