Machine Learning-Based Classification of Hybrid BCI Signals using Mayfly-Optimized Multiclass Weighted Random Forest

The Brain-Computer Interface (BCI) technologies have excellent clinical and non-clinical uses. Among the most popular imaging methods adopted in BCI technologies is electroencephalography (EEG). But EEG signals are typically quite complicated, so analyzing them necessitates a significant amount of effort. With the help of machine learning (ML), this research investigates the feasibility of a BCI platform based on the motor imagery (MI) concept. The steps of pre-processing, feature extraction and classification are the underpinning of any conventional ML model. To train such a model, however, a large amount of data is needed. To address this gap, this work introduces a new mayfly-optimized multiclass weighted random forest (MFO-MWRF) technique that uses retrieved features as input to mitigate the need for this supplementary data. In this study, we gather a dataset of hybrid EEG and fNIRS motor imagery that can be pre-processed using a Wiener filter (WF) to filter out noisier signals without affecting the high-quality images. The characteristics are extracted using the discrete wavelet transform (DWT). The research results indicate that the proposed approach achieves the best performance compared to existing approaches for classifying motor movement images

The Hybrid BCI

Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system

A Python-based Brain-Computer Interface Package for Neural Data Analysis

Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references. Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented

Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals

Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design. The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%. The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI

Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The BCI system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8±13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.University Starter Grant of the University of Waterloo (No. 203859) National Natural Science Foundation of China (Grant No. 51620105002

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs