Brain Computer Interface (BCI) technology converts central nervous system activity into commands that can control computer-based applications and replace natural outputs. Unfortunately, individuals with severe neuromuscular diseases like amyotrophic lateral sclerosis (ALS) may not be able to operate BCI applications that rely on eyesight. This study seeks to extend recent studies that use closed eyes to elicit a yes-no response based on steady state visual evoked potentials (SSVEPs). SSVEPs are electrical activity in the brain in response to repeated visual stimuli presented at a fixed rate. These signals have a low signal to noise ratio and thus were recorded on the scalp via Electroencephalogram (EEG).
In this study, five subjects were instructed to focus on either the left or the right eye as on- or off-center red-light emitting diodes presented frequency pairs (23, 29, 31 Hz) to each closed eye. The BCI controlled stimulus presentation and data collection. Fast Fourier Transform (FFT) analysis was conducted to extract and amplify EEG patterns associated with SSVEP responses. The results revealed a significant difference for diode location in optimal neuronal responses, as well as individual differences for frequency pairs and bands. This preliminary study demonstrated that SSVEPs measured with the eyes closed may be used for real-time communication at the bedside.