Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain computer interface (BCI) systems. In this study two sliding window techniques are proposed to enhance binary classification of motor imagery (MI). The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows which is named as SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a dataset of healthy individuals and on a stroke patients dataset. As compared to the existing state-of-the-art the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients dataset for left vs. right hand MI with lower standard deviation. For both the datasets the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window based prediction of MI using SW-LCR and SW-Mode is robust against inter-trial and inter-session inconsistencies in the time of activation within a trial and thus can lead to reliable performance in a neurorehabilitative BCI setting.

No abstract

Imagination of movement can be used as a control method for a brain-computer interface (BCI) allowing communication for the physically impaired. Visual feedback within such a closed loop system excludes those with visual problems and hence there is a need for alternative sensory feedback pathways. In the context of substituting the visual channel for the auditory channel, this study aims to add to the limited evidence that it is possible to substitute visual feedback for its auditory equivalent and assess the impact this has on BCI performance. Secondly, the study aims to determine for the first time if the type of auditory feedback method influences motor imagery performance significantly. Auditory feedback is presented using a stepped approach of single (mono), double (stereo), and multiple (vector base amplitude panning as an audio game) loudspeaker arrangements. Visual feedback involves a ball-basket paradigm and a spaceship game. Each session consists of either auditory or visual feedback only with runs of each type of feedback presentation method applied in each session. Results from seven subjects across five sessions of each feedback type (visual, auditory) (10 sessions in total) show that auditory feedback is a suitable substitute for the visual equivalent and that there are no statistical differences in the type of auditory feedback presented across five sessions.

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

No abstract

Objective: Magnetoencephalography (MEG) based Brain-Computer Interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation (CC), ReliefF (RF), Random Forest (RandF), and Infinite Latent Feature Selection (ILFS) were applied across six binary tasks in three different frequency bands) was evaluated in this study on two state-ofthe-art features i.e. bandpower and CSP. Main results: All four methods provided a statistically significant increase in classification accuracy (CA) compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12Hz), beta (13-30Hz), or broadband (α+β) (8-30Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the number of channels significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15-105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the number of channels will help to decrease the computation cost and maintain numerical stability in cases of low trial numbers. Significance: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.

No abstract

Motor imagery can be used to modulate sensorimotor rhythms (SMR) enabling detection of voltage fluctuations on the surface of the scalp using electroencephalographic electrodes. Feedback is essential in learning to modulate SMR for non-muscular communication using a brain-computer interface (BCI). A BCI not reliant upon the visual modality not only releases the visual channel for other uses but also offers an attractive means of communication for the physically impaired who are also blind or vision impaired. This study demonstrates the feasibility of replacing the traditional visual feedback modality with stereo auditory feedback. Results from a pilot study were used to select the most appropriate sounds for auditory feedback based on three options: broadband noise and two anechoic instrument samples. Subsequently, an SMR BCI was used to examine the effect on sensorimotor learning with broadband noise utilising a modified stereophonic presentation method. Twenty participants split into equal groups took part in ten sessions. The visual group performed best initially but did not improve over time whilst the auditory group improved as the study progressed. The results demonstrate the feasibility of using stereophonic auditory feedback with broadband noise as opposed to other auditory feedback presentation methods and sounds which are less intuitive.