Abstract
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities. Artifacts generated from body activities (e.g., eyeblinks and teeth clenches) often contaminate EEGs and make EEG-based classification/identification hard. Although independent component analysis (ICA) is the gold-standard technique for attenuating the effects of such contamination, the estimated independent components are still mixed with artifactual and neuronal information because ICA relies only on the independence assumption. The same problem occurs when using independent vector analysis (IVA), an extended ICA method. To solve this problem, we designed an independent low-rank matrix analysis (ILRMA)-based automatic artifact reduction technique that clearly models sources from observations under the independence assumption and a low-rank nature in the frequency domain. For automatic artifact reduction, we combined the signal separation technique with an independent component classifier for EEGs named ICLabel. To assess the comparative efficiency of the proposed method, the discriminabilities of artifact-reduced EEGs using ICA, IVA, and ILRMA were determined using an open-access EEG dataset named OpenBMI, which contains EEG data obtained through three BCI paradigms [motor-imagery (MI), event-related potential (ERP), and steady-state visual evoked potential (SSVEP)]. BCI performances were obtained using these three paradigms after applying artifact reduction techniques, and the results suggested that our proposed method has the potential to achieve higher discriminability than ICA and IVA for BCIs. In addition, artifact reduction using the ILRMA approach clearly improved (by over 70%) the averaged BCI performances using artifact-reduced data sufficiently for most needs of the BCI community. The extension of ICA families to supervised separation that leaves the discriminative ability would further improve the usability of BCIs for real-life environments in which artifacts frequently contaminate EEGs.
Highlights
An electroencephalogram (EEG)-based brain–computer interface (BCI) is a well-established technology that enables communicating with others without performing actual body movements by finding specific brain activity patterns from EEGs and converting these into predefined commands (Wolpaw et al, 2002)
We used this dataset because (1) EEGs in the three BCI paradigms were collected from the same participants, (2) each paradigm was conducted for 2 days, and (3) baseline analysis methods based on Matlab functions in the OpenBMI toolbox are available
When there was a difference in the artifact reduction performance, we highlighted the superior results in bold in the table
Summary
An electroencephalogram (EEG)-based brain–computer interface (BCI) is a well-established technology that enables communicating with others without performing actual body movements by finding specific brain activity patterns from EEGs and converting these into predefined commands (Wolpaw et al, 2002). Several paradigms are used for eliciting robust time-independent or -dependent potentials in EEGs, such as motor imagery (MI) (Pfurtscheller and Da Silva, 1999), event-related potential (ERP) (Squires et al, 1976), and steadystate visual evoked potential (SSVEP) (Regan, 1966) Along with these paradigms, developments in machine-learningbased classifiers/identifiers have contributed to improvements in finding specific (elicited) patterns. ICA algorithms comprehensively minimize the reconstruction error with a linear combination for an entire sequence of trials This approach overestimates sources for representing the latent waveform of an observation; the estimation leads to oversubtraction or spectral distortion of the EEG power (Wallstrom et al, 2004; Castellanos and Makarov, 2006). The results suggest that the proposed method can potentially achieve higher discriminability than ICA for BCIs
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