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Towards improving P300-based brain-computer interfaces: From desktop to mobile
| Content Provider | Semantic Scholar |
|---|---|
| Author | Obeidat, Qasem |
| Copyright Year | 2014 |
| Abstract | A brain-computer interface (BCI) enables a paralyzed user to interact with an external device through brain signals. A BCI measures identifies 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 classification accuracy was significantly improved 91% with the ZP VS 80.6% with |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://library.ndsu.edu/ir/bitstream/handle/10365/27367/Towards%20Improving%20P300-based%20Brain-Computer%20Interfaces%20From%20Desktop%20to%20Mobile.pdf?isAllowed=y&sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
