New mental tasks were investigated for suitability in Brain-Computer Interface (BCI). Electroencephalography (EEG) signals were collected and analyzed to identify these mental tasks. MS Windows-based software was developed for investigating and classifying recorded EEG data with unnecessary frequencies filtered out with Bandpass filtering. To identify the best feature vector construction method for a given mental task, feature vectors were constructed using Bandpower, Principal Component Analysis, and Downsampling separately. These feature vectors were then classified with Linear Discriminant Analysis, Linear Support Vector Machines, Critical Distance Classifiers, Nearest Neighbor Classifiers, and their Non-Linear counterparts to find the best-performing classifier. For comparison purposes, performances of already well-known mental tasks in the BCI community were computed along with that of new mental tasks introduced in this thesis. In the preliminary studies, it was found that the most promising new mental task which a BCI system could identify is the imagination of hitting a given square with an imaginary arrow from above (or below) and right, (or left) to the screen. The group of these mental tasks was named as 'Hit Series' (HS). A detailed investigation of HS was carried out and compared with the performance of Motor Imagery (MI) events which are the most heavily used mental tasks in EEG-based BCI systems. One subject achieved the maximum average performance for HS, 100 pct in the binary classifications while 99 pct in overall combined performance. The best average performances of the other two subjects for the same mental tasks were 93 pct and 87pct with the overall performance of 89 pct and 78 pct. Performances of the same three subjects for mental tasks in MI were relatively poor. The average performances were 92, 78, and 92 pct while overall performances were 87, 69, and 88 pct.

翻译：本研究探索了适用于脑机接口的新型心理任务。实验采集并分析了脑电图信号以识别这些心理任务。开发了基于Windows平台的软件系统，通过带通滤波滤除无关频率成分，对记录的脑电数据进行分类研究。为确定特定心理任务的最优特征向量构建方法，分别采用带功率、主成分分析和降采样技术构建特征向量，随后通过线性判别分析、线性支持向量机、临界距离分类器、最近邻分类器及其非线性对比方法进行分类，以筛选性能最优的分类器。作为对照，本文同步计算了脑机接口领域已有经典心理任务与新引入心理任务的分类性能。初步研究发现，最具潜力的新型心理任务是"想象用虚拟箭头从屏幕上方（或下方）及右侧（或左侧）击打指定方块"，该类任务被命名为"击打系列"。研究对HS任务进行了系统分析，并与基于脑电图系统的脑机接口中应用最广泛的心理任务——运动想象事件进行性能对比。其中一名受试者在二分类任务中达到HS任务最高平均性能（100%），综合性能为99%；另两名受试者最佳平均性能分别为93%和87%，综合性能为89%和78%。相同三名受试者在MI任务中的表现相对较差，平均性能分别为92%、78%和92%，综合性能为87%、69%和88%。