Abstract
ObjectiveBrain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.MethodsForty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.ResultsCorrelation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.ConclusionsEvent-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.SignificanceOur method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Highlights
Brain injuries or neurological diseases can lead to complete motor paralysis
Correlation between auditory oddball response and P300 brain-computer interfaces (BCIs) accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms
Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI
Summary
Brain injuries or neurological diseases (e.g. amyotrophic lateral sclerosis, ALS) can lead to complete motor paralysis. Depending on the degree of impairment caused by the injury or the progression of the disease communication can become very difficult and even impossible This loss of communicative abilities can be overcome with interfaces that bypass the need for muscular control and detect the user’s intentions directly from signals recorded from the brain. These brain-computer interfaces (BCIs) are currently used for communication and for restoration of motor control [1,2,3,4]. Disadvantages are the strong attenuation of the neural signals by the skull and skin and long preparation times if many EEG electrodes (w32) are applied. Up to now for working with severely paralyzed patients EEG, remains the most practical method
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