Does feedback modality affect performance of brain computer interfaces?

Abstract

Brain computer interfaces (BCI) are used for communication and rehabilitation. One of the main categories of BCI techniques is motor imagery based BCI (MI-BCI). A large number of studies have focused on machine learning approaches to optimize MI-BCI performance. However, enhancement of MI-BCI through provision of optimized feedback modalities has not received equal attention. Motor imagery and motor execution activate almost the same area of the brain. During motor skills performance, a combination of proprioceptive and direct visual feedback (PDVF) is provided. Thus, we hypothesized that MI-BCI that receives PDVF outperforms the traditional MI-BCI, which only uses indirect visual feedback (IVF). We studied 8 healthy subjects performing MI through (i) IVF and (ii) PDVF. We used 8 channel electroencephalogram (EEG) signals and extracted features using an autoregressive model and classified MIs using linear regression. On average, PDVF increased the accuracy of MI performance by 11%, and improved information transfer rate (ITR) by more than two times. In conclusion, using PDVF appears to improve MI-BCI performance according to the studied metrics, making this approach potentially more reliable.