Classification of EEG Signals Produced by RGB Colour Stimuli

Abstract

In this paper we have presented results for classification of electroencephalograph (EEG) signals produced by the random visual exposure of primary colours i.e. red, green and blue to the subject while sitting in a dark room. Event-related spectral perturbations (ERSP) are used as features for support vector machine (SVM). Our objective was to classify the EEG signals as Red, Green and Blue classes and we have successfully classified the three visual conditions having accuracy of 84%, 89% and 98% with linear, polynomial and radial basis function kernels respectively with in all the groups of data among all the subjects.

Decoding chromaticity and luminance from patterns of EEG activity

“…Neuroimaging work has demonstrated that the specific stimulus chromaticity can be decoded from patterns of fMRI BOLD activity in visual cortex (Brouwer & Heeger, 2009;2013), and there is broad interest in determining whether this information can be decoded from EEG activity. Recent work in the fields of working memory (Bocincova & Johnson, 2019) and brain-computer interface (BCI) development (Rasheed & Marini, 2015) aimed to decode the color of a stimulus from patterns of low frequency EEG activity on the scalp. VEPs, however, are well known to respond to differences in either stimulus chromaticity or luminance (Kulikowski et al, 1996;Skiba et al, 2014) and previous EEG and magnetoencephalography (MEG) work did not control for individual differences in luminance for the chromatic stimuli (Bocincova & Johnson, 2019;Rasheed & Marini, 2015;Sandhaeger et al, 2019).…”

“…Recent work in the fields of working memory (Bocincova & Johnson, 2019) and brain-computer interface (BCI) development (Rasheed & Marini, 2015) aimed to decode the color of a stimulus from patterns of low frequency EEG activity on the scalp. VEPs, however, are well known to respond to differences in either stimulus chromaticity or luminance (Kulikowski et al, 1996;Skiba et al, 2014) and previous EEG and magnetoencephalography (MEG) work did not control for individual differences in luminance for the chromatic stimuli (Bocincova & Johnson, 2019;Rasheed & Marini, 2015;Sandhaeger et al, 2019). Furthermore, an important test for a true chromatic signature is whether classification of chromaticity is maintained despite changes in the luminance of the chromatic stimuli.…”

Investigation of Visual Stimulus With Various Colors and the Layout for the Oddball Paradigm in Evoked Related Potential-Based Brain–Computer Interface

“…Moreover, the luminosity contrast was also investigated for P300 speller (Li et al, 2014). The RGB colors acting as stimuli have been utilized to compare EEG classification algorithms or feature extraction methods (Rasheed and Marini, 2015; Alharbi et al, 2016). However, the paradigm was limited to one square pattern responsible for presenting colors under a gray background, with a stimulus duration of 3 s one time, instead of the oddball paradigm.…”

Detection of primary RGB colors projected on a screen using fNIRS

“…It should come as no surprise, then, that color stimulation and detection in the visual cortex have been the subjects of long-standing study and debate. [1][2][3][4][5][6][7][8][9][10][11][12][13] The objective of the present study is to characterize appropriate features of the hemodynamic response (HR) signals obtained from the visual cortex upon color stimuli. Five di®erent features (i.e., mean, slope, peak, skewness, and kurtosis) of the obtained HR signals are examined in order to classify the three di®erent color stimuli (i.e., red, green, and blue (RGB)).…”

“…Rasheed and Marini, 12 applying an EEG modality to the classi¯cation of RGB-color stimuli, classi¯ed three visual conditions to 84%, 89% and 98% accuracies with linear, polynomial, and radial basis function kernels, respectively. Alharbi et al 13 similarly evaluated a single-trial classi¯cation model for RGB-color-stimulus-evoked EEG signals, and determined that the empirical mode decomposition residual provides the most accurate, fastest, and most reliable classi¯cation (average accuracy: 88.5%, execution time: 14 s).…”