Logar Vito, Skrjanc Igor, Belic Ales, Brezan Simon, Koritnik Blaz, Zidar Janez
Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, SI-1000 Ljubljana, Slovenia.
Artif Intell Med. 2008 Sep;44(1):41-9. doi: 10.1016/j.artmed.2008.06.003. Epub 2008 Jul 26.
The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information.
The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs.
In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data.
The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subject's performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome.
The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.
脑机接口（BCI）这一领域犹如一片广袤且大多仍未被探索的土地，但或许脑机接口最有趣的部分在于尝试理解大脑自身的信息交换与编码。根据近期的一些报道，信号的相位特征在信息传递和编码中起着重要作用。关于信息处理的相位偏移机制，也被称为信息的相位编码。
作者想要表明，当使用基于脑电图（EEG）数据相位解调的方法时，在不同握力控制任务执行过程中测量得到的EEG信号携带了足够的信息，能够成功预测受试者所施加的握力。由于所提出的方法是非侵入性的，它可以作为开发脑机接口的一种替代方法。
为了从EEG信号中预测握力，我们使用了一种方法，该方法采用后续的信号处理方法：简单滤波方法，用于提取合适的脑节律；主成分分析，用于实现线性独立并检测信号源；以及相位解调方法，用于提取关于握力的相位编码信息。然后使用模糊推理系统从处理后的EEG数据中预测握力。
所提出的方法清楚地表明，EEG信号携带了足够的信息来成功预测受试者的表现。此外，交叉验证表明，在测试的受试者之间，关于握力的信息以非常相似的方式进行编码。至于脑机接口的开发，考虑到预处理数据和训练模糊模型的计算时间，如果能够克服该方法的实时非因果限制，那么进行实时在线分析将是可能的。
该研究表明，人类大脑中的相位编码是视觉运动任务期间信息编码或传递的一种可能机制，而关于握力的相位编码内容可以使用相位解调方法成功提取。由于该方法已被证明适用于本研究的情况，它也可以作为开发用于类似任务的脑机接口的一种替代方法。
