  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.
