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  1. Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network
    Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M
    Sensors (Basel), 2021 Sep 30;21(19).
    PMID: 34640888 DOI: 10.3390/s21196570
    Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
    Matched MeSH terms: Brain-Computer Interfaces*
  2. Designing a neurofeedback device to quantify attention levels using coffee as a reward system
    Cheng KS, Lee JX, Lee PF
    Int J Occup Saf Ergon, 2021 Mar;27(1):258-266.
    PMID: 29658406 DOI: 10.1080/10803548.2018.1459348
    Purpose. Work performance is closely related to one's attention level. In this study, a brain-computer interface (BCI) device suitable for office usage was chosen to quantify the individual's attention levels. Methods. A BCI system was adopted to interface brainwave signals to a coffee maker via three ascending levels of laser detectors. The preliminary test with this prototype was to characterize the attention level through the collected coffee amount. Here, the preliminary testing was comparing the correlation between the attention level and the participants' cumulative grade point average (CGPA) and scores from the 21-item depression, anxiety, and stress scale (DASS-21) and the attentional control scale (ACS) using ordinal regression. It was assumed that a greater CGPA would generate a greater attention level. Result. The generated coffee amount from the BCI system had a significant positive correlation with the CGPA (p = 0.004), mild depression (p = 0.019) and mild and extremely severe anxiety (p = 0.044 and p = 0.019, respectively) and a negative correlation with the ACS score (p = 0.042). Conclusion. This simple and cost-effective prototype has the potential to enable everyone to know their immediate attention level and predict the possible correlation to their mental state.
    Matched MeSH terms: Brain-Computer Interfaces*
  3. EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation
    Al-Qazzaz NK, Alyasseri ZAA, Abdulkareem KH, Ali NS, Al-Mhiqani MN, Guger C
    Comput Biol Med, 2021 10;137:104799.
    PMID: 34478922 DOI: 10.1016/j.compbiomed.2021.104799
    Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.
    Matched MeSH terms: Brain-Computer Interfaces*
  4. Fulltext EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications
    Abdi Alkareem Alyasseri Z, Alomari OA, Al-Betar MA, Awadallah MA, Hameed Abdulkareem K, Abed Mohammed M, et al.
    Comput Intell Neurosci, 2022;2022:5974634.
    PMID: 35069721 DOI: 10.1155/2022/5974634
    Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.
    Matched MeSH terms: Brain-Computer Interfaces*
  5. Fulltext Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future
    Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, et al.
    Brain Comput Interfaces (Abingdon), 2017;4(1-2):3-36.
    PMID: 29152523 DOI: 10.1080/2326263X.2016.1275488
    The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
    Matched MeSH terms: Brain-Computer Interfaces
  6. Fulltext The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
    Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, et al.
    PeerJ Comput Sci, 2021;7:e374.
    PMID: 33817022 DOI: 10.7717/peerj-cs.374
    Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
    Matched MeSH terms: Brain-Computer Interfaces
  7. Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
    Nataraj SK, Paulraj MP, Yaacob SB, Adom AHB
    J Med Signals Sens, 2020 11 11;10(4):228-238.
    PMID: 33575195 DOI: 10.4103/jmss.JMSS_52_19
    Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain-computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance.

    Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.

    Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.

    Matched MeSH terms: Brain-Computer Interfaces
  8. Fulltext Steady state visual evoked potential based BCI as control method for exoskeleton: a review
    Norhafizan Ahmad, Raja Ariffin Raja Ghazilla, Muhammad Zikril Hakim Md Azizi
    Malaysian Journal of Public Health Medicine, 2016;16(10):0-0.
    MyJurnal
    Brain Computer Interfaces (BCI) provide a vast possibility in enabling the brain to communicate directly with the computer, hence providing an alternative in controlling the machines without much effort. In fields of rehabilitations robotics, the applications of an exoskeletons in assisting a spinal cord injured (SCI) patients were growing. Steady state visually evoked potentials (SSVEP) based BCIs that utilizes the human visual reactions to the constant flickered stimulus quickly showed its potentials among the BCIs used in rehabilitations devices because of its advantages such as a higher immunity to noises and artefacts and also its robustness compared to other BCIs. Rehabilitation exoskeletons demands an approach that are more user friendly and the aspects of control scheme and mechanical parts that are more focused on assisting the patients in rehabilitations and providing a SCI patients an alternatives to explore their surroundings in a more intuitive ways. This paper highlights the current development trends in SSVEP based BCIs for rehabilitation exoskeletons and proposed the potential research scopes in the future that can improve the effectiveness, and its potential applications in rehabilitations.
    Matched MeSH terms: Brain-Computer Interfaces
  9. HMM based automated wheelchair navigation using EOG traces in EEG
    Aziz F, Arof H, Mokhtar N, Mubin M
    J Neural Eng, 2014 Oct;11(5):056018.
    PMID: 25188730 DOI: 10.1088/1741-2560/11/5/056018
    This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.
    Matched MeSH terms: Brain-Computer Interfaces*
  10. Robust classification of motor imagery EEG signals using statistical time-domain features
    Khorshidtalab A, Salami MJ, Hamedi M
    Physiol Meas, 2013 Nov;34(11):1563-79.
    PMID: 24152422 DOI: 10.1088/0967-3334/34/11/1563
    The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain-machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time-domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time-domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature-classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy.
    Matched MeSH terms: Brain-Computer Interfaces*
  11. A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations
    Al-Qaysi ZT, Zaidan BB, Zaidan AA, Suzani MS
    Comput Methods Programs Biomed, 2018 Oct;164:221-237.
    PMID: 29958722 DOI: 10.1016/j.cmpb.2018.06.012
    CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.

    OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.

    METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.

    RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.

    DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.

    CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.

    Matched MeSH terms: Brain-Computer Interfaces*
  12. Fulltext Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal
    Darvish Ghanbar K, Yousefi Rezaii T, Farzamnia A, Saad I
    PLoS One, 2021;16(3):e0248511.
    PMID: 33788862 DOI: 10.1371/journal.pone.0248511
    Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
    Matched MeSH terms: Brain-Computer Interfaces*
  13. Fulltext Preliminary Study of Memory Enhancement of USIM Students with Internet Addiction Disorder by the Effect of Quran Therapy in Terms of EEG Power Spectra
    Saleh, N. N., Tamam, S.
    Physics and Technology in Medicine, 2020;1(1):45-51.
    MyJurnal
    The advancement of internet nowadays have drives the university students place great reliance on the internet for almost all matter. However, if the internet is heavily used, it may have a detrimental effect which could contribute to an Internet Addiction Disorders (IAD) among university students. IAD could create problem in developing relationships like normal people and appear to have low quality of memory. Thus, this is definitely not good to students. Knowing that by listening to Quran verse has a potential to improve the memory, hence this study is carried out. The study utilized a brain-computer interface Emotiv EPOC+, multichannel electroencephalography (EEG) detection during dual N-back tasks (spatial and verbal) where subjects were given with load level from 2-back to 3-back. Five (5) out of two hundred (200) participants of USIM’s students (20-23 years old) were recruited to continue with the brain scan experiment after getting a high score which indicated their severe level of addiction in a screening addiction test, Young’s Internet Addiction Test. Subjects were administered with a pre‐ and post‐tests to analyze and evaluate the effects of implementing Quran listening in their dailies for two months. The behavioral assessment exhibited accuracy increment between pre- and post- tests by 13.4% from 9.4%. While the EEG power value showed there were significant differences (p=0.002) in brainwaves between the pre- and the post- tests for 2 back; as the dominant brainwave found at the frontal and prefrontal cortex for delta and theta bands. The research revealed that the theta power band presented as the most dominant brain wave associated with N-back task for the enhancement of working memory which influence by the listening to Surah in Al-Quran.
    Matched MeSH terms: Brain-Computer Interfaces
  14. Fulltext Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements
    Al-Quraishi MS, Elamvazuthi I, Tang TB, Al-Qurishi M, Adil SH, Ebrahim M
    Brain Sci, 2021 May 27;11(6).
    PMID: 34071982 DOI: 10.3390/brainsci11060713
    Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.
    Matched MeSH terms: Brain-Computer Interfaces
  15. Fulltext Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification
    Lai CQ, Ibrahim H, Abdullah MZ, Abdullah JM, Suandi SA, Azman A
    Comput Intell Neurosci, 2019;2019:7895924.
    PMID: 31281339 DOI: 10.1155/2019/7895924
    Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.
    Matched MeSH terms: Brain-Computer Interfaces
  16. Effect of mindfulness meditation on brain-computer interface performance
    Tan LF, Dienes Z, Jansari A, Goh SY
    Conscious Cogn, 2014 Jan;23:12-21.
    PMID: 24275085 DOI: 10.1016/j.concog.2013.10.010
    Electroencephalogram based brain-computer interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and no-treatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects.
    Matched MeSH terms: Brain-Computer Interfaces/psychology*
  17. The Two-Brains Hypothesis: Towards a guide for brain-brain and brain-machine interfaces
    Goodman G, Poznanski RR, Cacha L, Bercovich D
    J Integr Neurosci, 2015 Sep;14(3):281-93.
    PMID: 26477360 DOI: 10.1142/S0219635215500235
    Great advances have been made in signaling information on brain activity in individuals, or passing between an individual and a computer or robot. These include recording of natural activity using implants under the scalp or by external means or the reverse feeding of such data into the brain. In one recent example, noninvasive transcranial magnetic stimulation (TMS) allowed feeding of digitalized information into the central nervous system (CNS). Thus, noninvasive electroencephalography (EEG) recordings of motor signals at the scalp, representing specific motor intention of hand moving in individual humans, were fed as repetitive transcranial magnetic stimulation (rTMS) at a maximum intensity of 2.0[Formula: see text]T through a circular magnetic coil placed flush on each of the heads of subjects present at a different location. The TMS was said to induce an electric current influencing axons of the motor cortex causing the intended hand movement: the first example of the transfer of motor intention and its expression, between the brains of two remote humans. However, to date the mechanisms involved, not least that relating to the participation of magnetic induction, remain unclear. In general, in animal biology, magnetic fields are usually the poor relation of neuronal current: generally "unseen" and if apparent, disregarded or just given a nod. Niels Bohr searched for a biological parallel to complementary phenomena of physics. Pertinently, the two-brains hypothesis (TBH) proposed recently that advanced animals, especially man, have two brains i.e., the animal CNS evolved as two fundamentally different though interdependent, complementary organs: one electro-ionic (tangible, known and accessible), and the other, electromagnetic (intangible and difficult to access) - a stable, structured and functional 3D compendium of variously induced interacting electro-magnetic (EM) fields. Research on the CNS in health and disease progresses including that on brain-brain, brain-computer and brain-robot engineering. As they grow even closer, these disciplines involve their own unique complexities, including direction by the laws of inductive physics. So the novel TBH hypothesis has wide fundamental implications, including those related to TMS. These require rethinking and renewed research engaging the fully complementary equivalence of mutual magnetic and electric field induction in the CNS and, within this context, a new mathematics of the brain to decipher higher cognitive operations not possible with current brain-brain and brain-machine interfaces. Bohr may now rest.
    Matched MeSH terms: Brain-Computer Interfaces
  18. Fulltext Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features
    Sweeti, Joshi D, Panigrahi BK, Anand S, Santhosh J
    J Healthc Eng, 2018;2018:9213707.
    PMID: 29808111 DOI: 10.1155/2018/9213707
    This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
    Matched MeSH terms: Brain-Computer Interfaces
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