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12th International Conference on the Developments in eSystems Engineering (DeSE)

作者： Jameel, Huda Farooq Mohammed, Salim Latif Gharghan, Sadik Kamel Middle Tech Univ Elect Engn Tech Coll Dept Med Instrumentat Tech Engn Baghdad Iraq

ISBN: (纸本)9781728130217

An electroencephalography (EEG)-based wheelchair control system (EEG-WCS) can serve the disabled in their life activities, particularly in assisting them in moving freely. Given the recent evolution of new technology, the disabled can move freely without requiring aid from others and communicate with their community by using advanced smart wheelchairs. In this paper, an EEG-WCS algorithm is proposed for controlling wheelchair movements based on EEG signals. Based on microwave radar sensors, the algorithm allows the wheelchair to avoid obstacles during movement. The EEG-WCS consists of an electric wheelchair, Emotiv INSIGHT brainw ear to read brain signals, a DC motor driver for controlling the velocity of the wheelchair, microcontroller, DC motor, and batteries. In addition, the C language can be used to program the system's controller (microcontroller). Moreover, we classify, explore, and highlight the recent solutions for such systems and compare them in terms of control method, algorithm used, sensor type, accuracy, and response time.

关键词： brain-computer-interface electroencephalograph insight emotiv microwave sensor microcontroller motor driver,wheelchair

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Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using Electrophysiological and Kinematic Activity

Estimating Cognitive Workload in an Interactive Virtual Real...

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作者： Tremmel, Chrisrtoph Old Dominion University

学位级别：Ph.D.

As virtual reality (VR) technology continues to gain prominence in commercial, educational, recreational and research applications, there is increasing interest in incorporating physiological sensors in VR devices for passive user-state monitoring to eventually increase the sense of immersion. By recording physiological signals such as the electroencephalogram (EEG), electromyography (EMG) or kinematic parameters during the use of a VR device, the user’s interactions in the virtual environment could be adapted in real time based on the user’s cognitive state. This dissertation evaluates the feasibility of passively monitoring cognitive workload via electrophysiological and kinematic activity while performing a classical n-back task in an interactive VR environment. The results indicate that scalp measurements of electrical activity and controller and headset tracking of kinematic activity can effectively discriminate three workload levels. Since motion and muscle tension can create co-varying task-related artifacts in EEG sensors mounted to the VR headset, decontamination algorithms were developed. The newly developed warp correlation filter (WCF) and linear regression denoising were applied on EEG, which could significantly decrease the influence of these artifacts. Analysis of the scalp recorded spectrum suggest two transient activity (termed pulse-decay effects) that impact feature extraction, modeling, and overall interpretation of workload estimation from scalp recordings. The best classification accuracy could be achieved by combining EMG, EEG and kinematic activity features using an artificial neural network (ANN).

关键词： brain-computer-interface Virtual reality Biomedical Engineering and Bioengineering

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IMPLEMENTATION OF EEG SIGNAL PROCESSING AND DECODING FOR TWO-CLASS MOTOR IMAGERY DATA

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2019年第4期31卷

作者： Al-Faiz, Mohammed Z. Al-Hamadani, Ammar A. Al Farahidi Univ Dept Univ Presidency Baghdad Iraq Al Iraqia Univ Dept Network Engn Baghdad Iraq

This work decodes two-class motor imagery (MI) based on four main processing steps: (i) Raw electroencephalographic (EEG) signal is decomposed to single trials and spatial filters are estimated for each trial by common spatial filtering (CSP) method;(ii) features are extracted by taking the log transformation (normal distribution) of the spatially filtered EEG signal;(iii) optimal channel selection algorithm is proposed to reduce the number of EEG channels, such approach is regarded as key technological advantage in the implementation of brain-computer interface (BCI) to reduce the system processing time;(iv) finally, support vector machine (SVM) is employed to discriminate two classes of left and right hand MI. Two variations of SVM were proposed: polynomial function kernel and radial-based function RBF kernel. The results revealed that CSP succeeded in removing the strong correlation bound between the EEG samples by maximizing the variance of class 2 samples while minimizing the variance of class 1 samples. The channel selection algorithm achieved its goal to reduce the data dimension by selecting two channels out of three having the lowest variance entropies of 0.239 and 0.261 for channel 1 and channel 2, respectively. The features vector was divided into 80% train and 20% test with five-fold cross validation. The classification performance of SVM-polynomial kernel was 87.86% while it is 95.72% for SVM-RBF kernel as average accuracy of five-folds for both. Thus SVM-RBF is superior to SVM-Poly in the proposed framework.

关键词： brain-computer-interface Support vector machine Common spatial pattern CSP Optimal channel selection

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The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity

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brain RESEARCH 2017年 1674卷 91-100页

作者： Mrachacz-Kersting, N. Voigt, M. Stevenson, A. J. T. Aliakbaryhosseinabadi, S. Jiang, N. Dremstrup, K. Farina, D. Aalborg Univ Dept Hlth Sci & Technol Ctr Sensory Motor Interact SMI Fredrik Bajers Vej 7 D-3 DK-9220 Aalborg Denmark Univ Waterloo Dept Syst Design Engn Waterloo ON Canada Imperial Coll London Dept Bioengn London England

A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the movement related cortical potential (MRCP) induces significant plasticity at the cortical level in healthy individuals and chronic stroke patients. Transferring this type of associative brain-computer interface (BCI) intervention into the clinical setting requires that the proprioceptive input is comparable to the techniques implemented during the rehabilitation process. These consist mainly of functional electrical stimulation (FES) and passive movement induced by an actuated orthosis. In this study, we compared these two interventions (BCIFEs and BCIpassive) where the afferent input was timed to arrive at the motor cortex during the PN of the MRCP. Twelve healthy participants attended two experimental sessions. They were asked to perform 30 dorsiflexion movements timed to a cue while continuous electroencephalographic (EEG) data were collected from FP1, Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, and Pz, according to the standard international 10-20 system. MRCPs were extracted and the PN time calculated. Next, participants were asked to imagine the same movement 30 times while either FES (frequency: 20 Hz, intensity: 8-35 mAmp) or a passive ankle movement (amplitude and velocity matched to a normal gait cycle) was applied such that the first afferent inflow would coincide with the PN of the MRCP. The change in the output of the primary motor cortex (M1) was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior (TA) muscle and measuring the motor evoked potential (MEP). Spinal changes were assessed pre and post by eliciting the TA stretch reflex. Both BCIFES and BCIpassive led to significant increases in the excitability of the cortical projections to TA (F-(2,F-22) = 4.44, p = 0.024) without any concomitant changes at the spinal level. These effects were still present 30 min after the cessation of both interventions. T

关键词： brain-computer-interface Afferent feedback Cortical plasticity

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Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors

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JOURNAL OF MEDICAL SYSTEMS 2017年第12期41卷 1-13页

作者： Samuel, Oluwarotimi Williams Geng, Yanjuan Li, Xiangxin Li, Guanglin Shenzhen Inst Adv Technol SIAT CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Biomed & Hlth Engn SIAT Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China

To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) of electromyogram (EMG) signals has been successfully applied. This technique requires amputees to provide sufficient EMG signals to decode their limb movement intentions (LMIs). However, amputees with neuromuscular disorder/high level amputation often cannot provide sufficient EMG control signals, and thus the applicability of the EMG-PR technique is limited especially to this category of amputees. As an alternative approach, electroencephalograph (EEG) signals recorded non-invasively from the brain have been utilized to decode the LMIs of humans. However, most of the existing EEG based limb movement decoding methods primarily focus on identifying limited classes of upper limb movements. In addition, investigation on EEG feature extraction methods for the decoding of multiple classes of LMIs has rarely been considered. Therefore, 32 EEG feature extraction methods (including 12 spectral domain descriptors (SDDs) and 20 time domain descriptors (TDDs)) were used to decode multiple classes of motor imagery patterns associated with different upper limb movements based on 64-channel EEG recordings. From the obtained experimental results, the best individual TDD achieved an accuracy of 67.05 +/- 3.12% as against 87.03 +/- 2.26% for the best SDD. By applying a linear feature combination technique, an optimal set of combined TDDs recorded an average accuracy of 90.68% while that of the SDDs achieved an accuracy of 99.55% which were significantly higher than those of the individual TDD and SDD at p < 0.05. Our findings suggest that optimal feature set combination would yield a relatively high decoding accuracy that may improve the clinical robustness of MDoF neuroprosthesis.

关键词： Upper limb prostheses Electromyography Electroencephalography Pattern recognition Motor imagery brain-computer-interface

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Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks

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JOURNAL OF NEURAL ENGINEERING 2016年第4期13卷 046017页

作者： Zink, Rob Hunyadi, Borbala Van Huffel, Sabine De Vos, Maarten Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Kasteelpk Arenberg 10 B-3001 Leuven Belgium iMinds Med IT Leuven Belgium Univ Oxford Dept Engn Oxford England

Objective. In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. Approach. We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. Main results. A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. Significance. Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

关键词： Mobile EEG auditory attention P300 cycling brain-computer-interface real-life environment EEG artifacts

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Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

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JOURNAL OF NEURAL ENGINEERING 2016年第2期13卷 026005页

作者： Zink, Rob Hunyadi, Borbala Van Huffel, Sabine De Vos, Maarten Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT Kasteelpk Arenberg 10 B-3001 Heverlee Belgium IMinds Med IT Leuven Belgium Univ Oxford Dept Engn Oxford England Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all D-26111 Oldenburg Germany

Objective. One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

关键词： mobile EEG brain-computer-interface tensor decompositions auditory P300 subject specific calibration free

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Evaluation Criteria of Biological Artifacts Removal Rate from EEG Signals

Evaluation Criteria of Biological Artifacts Removal Rate fro...

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IEEE 23rd International Symposium on Industrial Electronics (ISIE)

作者： Jafarifarmand, Aysa Badamchizadeh, Mohammad Ali Seyedarabi, Hadi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

ISBN: (纸本)9781479923991

EEG is one of the most important bioelectrical signals that plays a vital role in investigation of brain activities in clinical applications as well as brain-computer-interface systems. The major facing obstacle is that the EEG signals are often affected by a variety of large signal contaminations or artifacts, which reduce their usefulness. Various approaches have been introduced to remove the artifacts from EEG signals, but since the artifact-free EEG signal is practically not accessible as a comparison referent so the precise evaluation of the artifact removal level is not possible, which makes it impossible to realize how useful the obtained EEG signal is. However, several metrics have been proposed to measure the EEG artifact removal success rate. Various evaluation criteria are presented and analyzed in this paper.

关键词： EEG Artifacts brain-computer-interface Artifact Removal

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Comparing temporal aspects of visual, tactile, and microstimulation feedback for motor control

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JOURNAL OF NEURAL ENGINEERING 2014年第4期11卷 046025页

作者： Godlove, Jason M. Whaite, Erin O. Batista, Aaron P. Univ Pittsburgh Dept Bioengn Pittsburgh PA 15260 USA Ctr Neural Basis Cognit Pittsburgh PA USA Univ Pittsburgh Dept Neurosci Pittsburgh PA USA

Objectives. Current brain-computer interfaces (BCIs) rely on visual feedback, requiring sustained visual attention to use the device. Improvements to BCIs may stem from the development of an effective way to provide quick feedback independent of vision. Tactile stimuli, either delivered on the skin surface, or directly to the brain via microstimulation in somatosensory cortex, could serve that purpose. We examined the effectiveness of vibrotactile stimuli and microstimulation as a means of non-visual feedback by using a fundamental element of feedback: the ability to react to a stimulus while already in motion. Approach. Human and monkey subjects performed a center-out reach task which was, on occasion, interrupted with a stimulus cue that instructed a change in reach target. Main results. Subjects generally responded faster to tactile cues than to visual cues. However, when we delivered cues via microstimuation in a monkey, its response was slower on average than for both tactile and visual cues. Significance. Tactile and microstimulation feedback can be used to rapidly adjust movements mid-flight. The relatively slow speed of microstimulation is surprising and warrants further investigation. Overall, these results highlight the importance of considering temporal aspects of feedback when designing alternative forms of feedback for BCIs.

关键词： stimulation somatosensory feedback reaching brain-computer-interface

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Dynamic Mapping of brain and Cognitive Control of Virtual Gameplay (Study by Functional Magnetic Resonance Imaging)

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BULLETIN OF EXPERIMENTAL BIOLOGY AND MEDICINE 2013年第6期154卷 706-710页

作者： Rezakova, M. V. Mazhirina, K. G. Pokrovskiy, M. A. Savelov, A. A. Savelova, O. A. Shtark, M. B. Russian Acad Sci Siberian Branch Res Inst Int Tomog Ctr Novosibirsk Russia Russian Acad Sci Siberian Branch Res Inst Mol Biol & Biophys Novosibirsk Russia Comp Biofeedback Syst Res & Prod Co Novosibirsk Russia

Using functional magnetic resonance imaging technique, we performed online brain mapping of gamers, practiced to voluntary (cognitively) control their heart rate, the parameter that operated a competitive virtual gameplay in the adaptive feedback loop. With the default start picture, the regions of interest during the formation of optimal cognitive strategy were as follows: Brodmann areas 19, 37, 39 and 40, i.e. cerebellar structures (vermis, amygdala, pyramids, clivus). "Localization" concept of the contribution of the cerebellum to cognitive processes is discussed.

关键词： functional magnetic resonance imaging cognitive functions neuronal ensembles brain-computer-interface neurobiofeedback

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