Emotion-Inducing Imagery Versus Motor Imagery for a Brain-Computer Interface

Neural correlates of intentionally induced human emotions may offer alternative imagery strategies to control brain-computer interface (BCI) applications. In this paper, a novel BCI control strategy i.e., imagining fictional or recalling mnemonic sad and happy events, emotion-inducing imagery (EII), is compared to motor imagery (MI) in a study involving multiple sessions using a two-class electroencephalogram (EEG)-based BCI paradigm with 12 participants. The BCI setup enabled online continuous visual feedback presentation in a game involving one-dimensional control of a game character. MI and EII are compared across different signal-processing frameworks which are based on neural-time-series-prediction-preprocessing (NTSPP), filter bank common spatial patterns (FBCSP) and hemispheric asymmetry (ASYM). Online single-trial classification accuracies (CA) results indicate that MI performance across all participants is 77.54% compared to EII performance of 68.78% ( ${p} < 0.05$ ). The results show that an ensemble of the NTSPP, FBCSP and ASYM frameworks maximizes performance for EII with average CA of 71.64% across all participants. Furthermore, the participants' subjective responses indicate that they preferred MI over emotion-inducing imagery (EII) in controlling the game character, and MI was perceived to offer most control over the game character. The results suggest that EII is not a viable alternative to MI for the majority of participants in this study but may be an alternative imagery for a subset of BCI users based on acceptable EII performance (CA >70%) observed for some participants.