Decoding of top-down cognitive processing for SSVEP-controlled BMI

Abstract

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance

“…Typically, BCI systems based on the modulation of SMR using MI tasks have lower rates of efficiency than other BCI paradigms based on evoked potentials such as event-related potentials (ERP) or steady-state visual potentials (SSVEP) (Chen et al, 2015;Min et al, 2016;Nierhaus et al, 2019). This is because MI-based BCI users normally need to acquire the skill to efficiently perform the MI tasks.…”

Harmonic Amplitude Summation for Frequency-tagging Analysis

“…At present, although higher harmonic responses are an integral part of brain responses, they are not systematically addressed in frequency-tagging research. In many studies, higher harmonic responses are not even reported (e.g., Bekhtereva, Pritschmann, Keil, & Müller, 2018;Min, Dähne, Ahn, Noh, & Müller, 2016;Paulk, Kirszenblat, Zhou, & van Swinderen, 2015;Coia, Jones, Duncan, & Crognale, 2014;Kuś et al, 2013;Cottereau et al, 2011;Wattam-Bell et al, 2010;Di Russo et al, 2007;Müller et al, 2006;Braddick, Birtles, Wattam-Bell, & Atkinson, 2005;Chen, Seth, Gally, & Edelman, 2003;Heinrich & Bach, 2001;Regan & Regan, 1988a;Tononi et al, 1998;Peterzell & Norcia, 1997;Müller, Teder, & Hillyard, 1997;Morgan, Hansen, & Hillyard, 1996, or are extirpated by narrow [band-pass, Gabor, etc. ] filtering, e.g., Davidson, Mithen, Hogendoorn, van Boxtel, & Tsuchiya, 2020;Miskociv & Keil, 2013;Anderson & Müller, 2010;Regan, 1975).…”

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

“…Nakanishi et al (2014) implemented a 32-target SSVEP paradigm based on this coding strategy, which was further used to implement a 40-target SSVEP paradigm (Nakanishi et al 2017a). Instead of using a rectangle stimulus array, Min et al (2016) designed an SSVEP paradigm with a grid-shaped line array. Instead of gazing at a single flickering stimulus at a time, Tang et al (2019) designed the multi-focal SSVEPs (mfSSVEPs) paradigm that requires the subject to receive multiple flickers with different frequencies simultaneously.…”