Abstract
A brain-computer interface (BCI) translates brain signals into commands for the control of devices and for communication. BCIs enable persons with disabilities to communicate externally. Positive and negative affective sounds have been introduced to P300-based BCIs; however, how the degree of valence (e.g., very positive or positive) influences the BCI has not been investigated. To further examine the influence of affective sounds in P300-based BCIs, we applied sounds with five degrees of valence to the P300-based BCI. The sound valence ranged from very negative to very positive, as determined by Scheffe's method. The effect of sound valence on the BCI was evaluated by waveform analyses, followed by the evaluation of offline stimulus-wise classification accuracy. As a result, the late component of P300 showed significantly higher point-biserial correlation coefficients in response to very positive and very negative sounds than in response to the other sounds. The offline stimulus-wise classification accuracy was estimated from a region-of-interest. The analysis showed that the very negative sound achieved the highest accuracy and the very positive sound achieved the second highest accuracy, suggesting that the very positive sound and the very negative sound may be required to improve the accuracy.
Highlights
A brain-computer interface (BCI), referred to as a brain-machine interface (BMI), translates human brain signals into commands that can be used to control assistive devices or communicate externally with others (Wolpaw et al, 2000, 2002)
event-related potentials (ERPs) with P300 have been applied to BCIs, and such a system is called a P300-based BCI (Farwell and Donchin, 1988)
This study demonstrated that the r2 values for the late components of P300 displayed by the very positive or very negative sounds were highest around 400–700 ms and centered around the Cz channel
Summary
A brain-computer interface (BCI), referred to as a brain-machine interface (BMI), translates human brain signals into commands that can be used to control assistive devices or communicate externally with others (Wolpaw et al, 2000, 2002). BCIs are used to help persons with disabilities interact with the external world (Birbaumer and Cohen, 2007). BCIs decode brain signals obtained by invasive measurements such as electrocorticography or by non-invasive measurements such as scalp electroencephalography (EEG) (Pfurtscheller et al, 2008). One of the most successful BCIs utilizes EEG signals in response to stimuli – i.e., event-related potentials (ERPs). When a subject discriminates a rarely encountered stimulus from a frequently encountered stimulus, the ERP elicits a positive peak at around 300 ms from stimulus onset; this peak is named the P300 component. ERPs with P300 have been applied to BCIs, and such a system is called a P300-based BCI (Farwell and Donchin, 1988)
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