| dc.description.abstract | A brain-computer interface (BCI) enables a paralyzed user to interact with an
external device through brain signals. A BCI measures identi es patterns within
these measured signals, translating such patterns into commands. The P300 is a
pattern of a scalp potentials elicited by a luminance increment of an attended target
rather than a non-target character of an alphanumeric matrix. The Row-Column
Paradigm (RCP) can utilize responses to series of illuminations of matrix target
and non-target characters to spell out alphanumeric strings of P300-eliciting target
characters, yet this popular RCP speller faces three challenges. The adjacent problem
concerns the proximity of neighboring characters, the crowding problem concerns their
number. Both adjacent and crowding problems concern how these factors impede BCI
performance. The fatigue problem concerns how RCP use is tiring. This dissertation
addressed these challenges for both desktop and mobile platforms. A new P300 speller interface, the Zigzag Paradigm (ZP), reduced the adjacent problem by increasing the distance between adjacent characters, as well as the crowding
problem, by reducing the number neighboring characters. In desktop study, the
classi cation accuracy was signi cantly improved 91% with the ZP VS 80.6% with
the RCP. Since the ZP is not suitable for mobile P300 spellers with a small screen size, a
new P300 speller interface was developed in this study, the Edges Paradigm (EP). The
EP reduced the adjacent and crowding problems by adding
ashing squares located upon the outer edges of the character matrix in the EP. The classi cation accuracy of
the EP (i.e., 93.3%) was signi cantly higher than the RCP (i.e., 82.1%). We further
compared three speller paradigms (i.e., RCP, ZP, and EP), and the result indicated
that the EP produced the highest accuracy and caused less fatigue. Later, the EP
is implemented in a simulator of a Samsung galaxy smart phone on the Microsoft
Surface Pro 2. The mobile EP was compared with the RCP under the mobility
situation when a user is moving on a wheelchair. The results showed that the EP
signi cantly improved the online classi cation accuracy and user experience over the
RCP. | en_US |
