Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Informal support is essential for enabling many older people to age in place. However, there is limited scholarship examining the information needs of older adults’ informal support networks and how these could be met through home monitoring and information and communication technologies. Objective: The purpose of this study was to investigate how technologies that connect older adults to their informal and formal support networks could assist aging in place and enhance older adults’ health and wellbeing. Methods: Semi-structured interviews were conducted with ten older adults and a total of 31 members of their self-identified informal support networks. They were asked questions about their information needs and how technology could support the older adults to age in place. The interviews were transcribed and thematically analysed. Results: The analysis identified three overarching themes: (1) the social enablers theme, which outlined how timing, informal support networks, and safety concerns assist the older adults’ uptake of technology, (2) the technology concerns theme, which outlined concerns about cost, usability, information security and privacy, and technology superseding face-to-face contact, and (3) the information desired theme, which outlined what information should be collected and transferred and who should make decisions about this Conclusions: Older adults and their informal support networks may be receptive to technologies that monitor older adults within the home if it enables aging in place for longer. However, cost, privacy, security and usability barriers would need to be considered and the system should be individualisable to older adults’ changing needs. The user requirements identified from this study and described in this paper have informed the development of a technology which is currently being prototyped.

(2011). Qualitative Spatial and Temporal Reasoning: Emerging Applications, Trends, and Directions. Spatial Cognition & Computation: Vol. 11, Qualitative Spatial and Temporal Reasoning: Emerging Applications, Trends, and Directions, pp. 1-14.

Electroencephalogram (EEG) based motor imagery brain–computer interface (BCI) requires large number of subject specific training trials to calibrate the system for a new subject. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of a brain–computer interface is to reduce calibration time or completely eliminate it. To address this problem, existing approaches use covariance matrices of electroencephalography (EEG) trials as descriptors for decoding BCI but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing SPD based classification approach. However, SPD-based classification has limited applicability in small training sets because the dimensionality of covariance matrices is large in proportion to the number of trials. To overcome this drawback, our paper proposes a new framework that transforms SPD matrices in lower dimension through spatial filter regularized by prior information of EEG channels. The efficacy of the proposed approach was validated on the small sample scenario through Dataset IVa from BCI Competition III. The proposed approach achieved mean accuracy of $86.13\%$ and mean kappa of $0.72$ on Dataset IVa. The proposed method outperformed other approaches in existing studies on Dataset IVa. Finally, to ensure the robustness of the proposed method, we evaluated it on Dataset IIIa from BCI Competition III and Dataset IIa from BCI Competition IV. The proposed method achieved mean accuracy $92.22\%$ and $81.21\%$ on Dataset IIIa and Dataset IIa, respectively.

Motor imagery based electroencephalogram (EEG) brain computer interface (BCI) performance suffers from huge variations within and across subjects. This is due to different spatial and temporal characteristics among the subjects. To address these variabilities, a large number of labeled subject specific training trials are collected to calibrate systems for new subjects. This results in long calibration time that limit the BCI usage in practice. One major challenge in the development of brain computer interface is to reduce calibration time or completely eliminate it. Existing approaches rise up to this challenge by incorporating inter-subject and intra-subject variations through covariance matrices from other subject’s training trials. Current approaches use covariance matrices but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing Riemannian approach. However, in Riemannian approach the performance degrades in small sample scenario as dimensionality of covariance matrices is large in comparison to number of trials. To overcome this limitation, we proposed a new framework that transform covariance matrices into lower dimension through spatial filter regularized by data from other subjects. The efficacy of the proposed approach was validated on the small sample scenario dataset IVa from BCI competition III. The proposed approach has achieved 87.21% mean accuracy and 0.74 mean kappa on dataset IVa. The proposed method outperforms the conventional method and other existing studies on dataset IVa. To ensure the robustness of the proposed method we evaluated on dataset IIIa from BCI competition III and dataset IIa from BCI competition IV. The proposed method has achieved mean accuracy 90.93% and 80.98% on dataset IIIa and dataset IIa respectively.