Raza H, Rathee D, Zhou SM, Cecotti H, Prasad G.
Elsevier, (2019), pg 154-166
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.
Optimising network modelling methods for fMRI
1 May 2020
Pervaiz U, Vidaurre D, Woolrich MW, Smith SM. NeuroImage Volume 211, 1 May 2020 Abstract A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between...
Developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality voice
9 May 2019
Arora S, Baghai-Ravary L, Tsanas A. The Journal of the Acoustical Society of America 145, 2871 (2019) Abstract Recent studies have demonstrated that analysis of laboratory-quality voice...
Digital device link to health records could be key to answering major health challenges
8 February 2023
Health insights gained from smart tech and wearable devices could be bolstered dramatically by safe and secure linkage to electronic health records, according to researchers at the BHF Data...