Single trial analysis of slow cortical potentials: a study on anticipation related potentials

Abstract

Objective. Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. Approach. We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. Main results. We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. Significance. We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.

Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications

“…On the other hand, as mentioned earlier, the shape of the designed reference signal plays an important role in the performance of cICA. Therefore, investigation of the effect of other types of reference signals such as the common rectangular pulse, smoothed MRCPs (Garipelli et al, 2013), and discriminative-based reference signal (Lee et al, 2016) will be done in the future in attempt to improve detection performance.…”

Single trial prediction of self-paced reaching directions from EEG signals

“…Such a level of decoding requires proper pre-processing techniques (Garipelli et al, 2013) to enhance the SNR of SCPs through the use of spectral and spatial filters. This finding is consistent with previous works on detection of movement intention (Lew et al, 2012a) and movement execution (Niazi et al, 2012; Robinson et al, 2013; Xu et al, 2014), which showed the advantage of low frequency EEG components.…”

“…For signals below 7 Hz, we directly used time domain features (EEG amplitude). In particular, for the SCP, Garipelli et al (2013) have compared various spatial and spectral filtering methods to enhance the signal to noise ratio (SNR) of the slow potentials. Their results have shown higher separability index with the use of narrow pass-band filters between [0.1–1] Hz.…”

Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke

“…MRCPs are quite versatile, since their amplitudes and time courses vary depending on the subject’s psychological status as well as characteristics of the movement being performed such as distance, speed, precision, etc. [8], [9]. Traditionally, MRCPs have been studied by averaging over a large number of trials, since there was high trial-to-trial variability resulting from background neural activity and non-neural artifacts.…”