Abstract
For individuals with high degrees of motor disability or locked-in syndrome, it is impractical or impossible to use mechanical switches to interact with electronic devices. Brain computer interfaces (BCIs) can use motor imagery to detect interaction intention from users but lack the accuracy of mechanical switches. Hence, there exists a strong need to improve the accuracy of EEG-based motor imagery BCIs attempting to implement an on/off switch. Here, we investigate how monitoring the pupil diameter of a person as a psycho-physiological parameter in addition to traditional EEG channels can improve the classification accuracy of a switch-like BCI. We have recently noticed in our lab (work not yet published) how motor imagery is associated with increases in pupil diameter when compared to a control rest condition. The pupil diameter parameter is easily accessible through video oculography since most gaze tracking systems report pupil diameter invariant to head position. We performed a user study with 30 participants using a typical EEG based motor imagery BCI. We used common spatial patterns to separate motor imagery, signaling movement intention, from a rest control condition. By monitoring the pupil diameter of the user and using this parameter as an additional feature, we show that the performance of the classifier trying to discriminate motor imagery from a control condition improves over the traditional approach using just EEG derived features. Given the limitations of EEG to construct highly robust and reliable BCIs, we postulate that multi-modal approaches, such as the one presented here that monitor several psycho-physiological parameters, can be a successful strategy in making BCIs more accurate and less vulnerable to constraints such as requirements for long training sessions or high signal to noise ratio of electrode channels.
Highlights
Individuals in advance stages of motor neuron disease, those suffering specific types of brain stem strokes that lead to locked-in syndrome, or those with other severe motor disability, are unable to generate voluntary muscle movements
We do this by using the pupil diameter of subjects engaged in motor imagery as an additional feature to a linear classifier on top of the EEG-derived features employed by traditional Brain computer interfaces (BCIs) classifiers
Considerable progress has been made over the last decades to improve the accuracy of EEG-based BCIs, performance remains sub-optimal when compared to mechanical interaction devices such as mice or switches
Summary
Individuals in advance stages of motor neuron disease, those suffering specific types of brain stem strokes that lead to locked-in syndrome, or those with other severe motor disability, are unable to generate voluntary muscle movements. Several types of neuro-imaging tools have been traditionally used to build functional BCIs: electroencephalography (EEG) [1], functional magnetic resonance imaging (fMRI) [2], magneto-encephalography (MEG) [3], functional near infrared spectroscopy (fNIR) [4], electro-corticography [5] and intra-cortical recordings [6] All of these neuro-imaging techniques have been shown in the literature capable of generating a BCI based mental switch. We build upon previous findings of pupil dilation during mental imagery [7] in order to improve the performance of an EEG-based mental switch We do this by using the pupil diameter of subjects engaged in motor imagery as an additional feature to a linear classifier on top of the EEG-derived features employed by traditional BCI classifiers. The pupil diameter time series is captured using a remote video-based eye tracker
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