Krusienski Dean J, Shih Jerry J
School of Engineering at the University of North Florida, Jacksonville, FL 32224, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6019-22. doi: 10.1109/IEMBS.2010.5627603.
This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci. The patient performed one experimental session using the BCI spelling paradigm controlled by scalp-recorded EEG prior to the ECoG grid implantation, and one identical session controlled by ECoG after the grid implantation. The patient was able to achieve near perfect spelling accuracy using EEG and ECoG. An offline analysis of the average ERPs was performed to assess how accurately the average EEG ERPs could be predicted from the ECoG data. The preliminary results indicate that EEG ERPs can be accurately estimated from proximal asynchronous ECoG data using simple linear spatial models.
本研究对使用脑机接口(BCI)拼写器从一名患者记录的脑电图(EEG)和皮质脑电图(ECoG)事件相关电位(ERP)之间的关系进行了初步分析。该患者患有药物难治性癫痫,并接受了颅内ECoG网格电极阵列的临时植入以定位癫痫病灶。在植入ECoG网格之前,患者使用由头皮记录的EEG控制的BCI拼写范式进行了一次实验,在植入网格之后,又进行了一次由ECoG控制的相同实验。患者使用EEG和ECoG能够实现近乎完美的拼写准确性。对平均ERP进行了离线分析,以评估从ECoG数据中预测平均EEG ERP的准确程度。初步结果表明,使用简单的线性空间模型,可以从近端异步ECoG数据中准确估计EEG ERP。