Abstract
Objective. Brain computer interface (BCI) technology can be important for those unable to communicate due to loss of muscle control. Given that the P300 Speller provides a relatively slow rate of communication, highly accurate classification is of great importance. Previous studies have shown that alternative stimuli (e.g. faces) can improve BCI speed and accuracy. The present study uses two new alternative stimuli, locations and graspable tools. Functional MRI studies have shown that images of familiar locations produce brain responses in the parahippocampal place area and graspable tools produce brain responses in premotor cortex. Approach. The current studies show that location and tool stimuli produce unique and discriminable brain responses that can be used to improve offline classification accuracy. Experiment 1 presented face stimuli and location stimuli and Experiment 2 presented location and tool stimuli. Main results. In both experiments, offline results showed that a stimulus specific classifier provided higher accuracy, speed, and bit rate. Significance. This study was used to provide preliminary offline support for using unique stimuli to improve speed and accuracy of the P300 Speller. Additional experiments should be conducted to examine the online efficacy of this novel paradigm.
Highlights
Brain Computer InterfacesBrain Computer Interfaces (BCIs) involve the measurement of neural signals produced by the electrical activity of the brain, a method or algorithm applied to decode these signals, and a systematic method for applying the decoded signals to a behavior (Sajda, Müller, & Shenoy, 2008)
While statistical analyses indicated a significant difference in the P300 responses produced by the two different images, there was no indication of a statistical difference between the Face image and the White House images for the N170 and N400 responses
Despite there being an indication of only significant differences between the two image types in regards to the P300 response, it is possible that the BCI system may be sensitive enough to detect slight differences between the N170 and N400 responses produced by the two different image types
Summary
Brain Computer InterfacesBrain Computer Interfaces (BCIs) involve the measurement of neural signals produced by the electrical activity of the brain, a method or algorithm applied to decode these signals, and a systematic method for applying the decoded signals to a behavior (Sajda, Müller, & Shenoy, 2008). Since researchers discovered the ability to apply these signals to operate an external device, the potential applications of BCI technology continues to grow The uses of these recorded signals to operate BCIs can range from controlling external devices such as a robotic arm, to moving computer cursors, to creating works of art known as “Brain-Painting” (Münßinger et al, 2010;Velliste, Perel, Spalding, Whitford, & Schwartz, 2008). These types of devices have the potential to be useful for individuals who have lost limbs or muscle movement that is necessary for daily functioning. The P300 Speller, for examples, is a BCI communication method that can allow severely disabled individuals to convey their thoughts without having to rely on crucial muscle movement
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