Abstract
We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern (RCSP). EEG from five healthy individuals (Dataset IVa, BCI Competition III) were evaluated by a cross-subject paradigm. Prediction performance was evaluated via a two-layer feed-forward neural network, where the classifier was trained and tested by data from two subjects independently. On average, the overall mean prediction accuracies obtained using all 118 channels are (55.98±6.53) and (71.20±5.32) in cases of CSP and RCSP, respectively, which are slightly lower than the accuracies obtained using only the selected channels, i.e., (58.95±6.90) and (71.41±6.65), respectively. The highest mean prediction accuracy achieved for a specific subject pair by using selected EEG channels was on average (90.36±5.59) and outperformed that achieved by using all available channels (86.07 ± 10.71). Spatially projected cortical sources approximated using wMEM may be useful for capturing inter-subject associative sensorimotor brain dynamics and pave the way toward an enhanced subject-independent BCI.
Highlights
Most brain computer interfaces (BCI) require subject-specific training sessions, which can annoy users and limit BCI applications such as affective states assessment (Andujar et al, 2015), lie detection (Wang et al, 2016), and gaming
We show that wavelet-based maximum entropy on the mean (wMEM) captures associative intersubject sensorimotor dynamics, which can be utilized to assess inter-subject cortical associativity
We used dataset IVa of BCI Competition III, comprising EEG of five healthy subjects specified as aa, al, av, aw, and ay recorded during right hand and right foot motor imagery (MI) (Blankertz et al, 2006)
Summary
Most brain computer interfaces (BCI) require subject-specific training sessions, which can annoy users and limit BCI applications such as affective states assessment (Andujar et al, 2015), lie detection (Wang et al, 2016), and gaming (van de Laar et al, 2013). Inter-subject Associative Cortical Sources long-term brain signal variation over time and across individuals (Goncalves et al, 2006; Ahn and Jun, 2015; Kasahara et al, 2015; Zhang et al, 2015; Acqualagna et al, 2016; Athanasiou et al, 2017). Resting state electroencephalography (EEG)-derived spectral entropy and power spectral density are associated with sensorimotor BCI performance (Zhang et al, 2015; Acqualagna et al, 2016). Attention and motivation are psychological predictors that reflect sensorimotor BCI performance (Hammer et al, 2012). Taking anatomical information such as electrode positioning and head morphologies into consideration can augment subject-to-subject transfer learning and BCI performance (Wronkiewicz et al, 2015)
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