Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency

TY - JOUR

T1 - Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency

AU - Maye, Alexander

AU - Zhang, Dan

AU - Engel, Andreas K

PY - 2017/7

Y1 - 2017/7

N2 - In brain-computer interfaces (BCIs) that use the steady-state visual evoked response (SSVEP), the user selects a control command by directing attention overtly or covertly to one out of several flicker stimuli. The different control channels are encoded in the frequency, phase, or time domain of the flicker signals. Here, we present a new type of SSVEP BCI, which uses only a single flicker stimulus and yet affords controlling multiple channels. The approach rests on the observation that the relative position between the stimulus and the foci of overt attention result in distinct topographies of the SSVEP response on the scalp. By classifying these topographies, the computer can determine at which position the user is gazing. Offline data analysis in a study on 12 healthy volunteers revealed that 9 targets can be recognized with about 95±3% accuracy, corresponding to an information transfer rate (ITR) of 40.8 ± 3.3 b/min on average. We explored how the classification accuracy is affected by the number of control channels, the trial length, and the number of EEG channels. Our findings suggest that the EEG data from five channels over parieto-occipital brain areas are sufficient for reliably classifying the topographies and that there is a large potential to improve the ITR by optimizing the trial length. The robust performance and the simple stimulation setup suggest that this approach is a prime candidate for applications on desktop and tablet computers.

AB - In brain-computer interfaces (BCIs) that use the steady-state visual evoked response (SSVEP), the user selects a control command by directing attention overtly or covertly to one out of several flicker stimuli. The different control channels are encoded in the frequency, phase, or time domain of the flicker signals. Here, we present a new type of SSVEP BCI, which uses only a single flicker stimulus and yet affords controlling multiple channels. The approach rests on the observation that the relative position between the stimulus and the foci of overt attention result in distinct topographies of the SSVEP response on the scalp. By classifying these topographies, the computer can determine at which position the user is gazing. Offline data analysis in a study on 12 healthy volunteers revealed that 9 targets can be recognized with about 95±3% accuracy, corresponding to an information transfer rate (ITR) of 40.8 ± 3.3 b/min on average. We explored how the classification accuracy is affected by the number of control channels, the trial length, and the number of EEG channels. Our findings suggest that the EEG data from five channels over parieto-occipital brain areas are sufficient for reliably classifying the topographies and that there is a large potential to improve the ITR by optimizing the trial length. The robust performance and the simple stimulation setup suggest that this approach is a prime candidate for applications on desktop and tablet computers.

KW - Adult

KW - Algorithms

KW - Attention

KW - Brain Mapping

KW - Brain-Computer Interfaces

KW - Electroencephalography

KW - Evoked Potentials, Visual

KW - Female

KW - Fixation, Ocular

KW - Flicker Fusion

KW - Humans

KW - Male

KW - Pattern Recognition, Automated

KW - Photic Stimulation

KW - Reproducibility of Results

KW - Retinal Ganglion Cells

KW - Sensitivity and Specificity

KW - Visual Cortex

KW - Evaluation Studies

KW - Journal Article

U2 - 10.1109/TNSRE.2017.2666479

DO - 10.1109/TNSRE.2017.2666479

M3 - SCORING: Journal article

C2 - 28459691

VL - 25

SP - 1026

EP - 1036

JO - IEEE T NEUR SYS REH

JF - IEEE T NEUR SYS REH

SN - 1534-4320

IS - 7

ER -