Abstract
Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.
Highlights
Brain-computer interfaces (BCI) allow end-users to interact with a system using modulation of brain activities which are partially observable in electroencephalographic (EEG) signals (Wolpaw and Wolpaw, 2012)
The results of the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS) are composed of two scores: a Kinesthetic Motor Imageries (KMI) score and a Visual Motor Imageries (VMI) score, calculated according to the seven items which correspond to motor imagery, respectively kinesthetic and visual, performed by the subjects
The average classification accuracy between a right-hand KMI and a rest period was computed for 4 different classifiers (MDRM, CSP+Linear Discriminant Analysis (LDA), gfMDRM, TS+LR, see Table 1)
Summary
Brain-computer interfaces (BCI) allow end-users to interact with a system using modulation of brain activities which are partially observable in electroencephalographic (EEG) signals (Wolpaw and Wolpaw, 2012). A major modality of interaction is the detection of voluntary modulations in sensorimotor rhythms during Motor Imagery (MI) These sensorimotor rhythms are characterized, before and during an imagined movement, by a gradual decrease of power in—mainly—the mu-alpha (7–13 Hz) and beta (15–30 Hz) band and after the end of the motor imagery, by an increase of power in the beta band. These modulations are respectively known as EventRelated Desynchronization (ERD) and Event-Related Synchronization (ERS) or post-movement beta rebound (Pfurtscheller, 2003; Hashimoto and Ushiba, 2013; Kilavik et al, 2013; Lotte and Congedo, 2016). A KMI can be described as the ability to imagine performing a
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