المؤلفون: Greg G. Cook, Elizabeth Milne, Aleksandr Zaitcev, Wei Liu, Martyn N.J. Paley

المصدر: EEG Signal Processing: Feature extraction, selection and classification methods ISBN: 9781785613708

مصطلحات موضوعية: medicine.diagnostic_test, Computer science, business.industry, Feature extraction, Pattern recognition, Eeg classification, Electroencephalography, Source reconstruction, Motor imagery, Feature (computer vision), medicine, Artificial intelligence, business, Brain–computer interface

الوصف: The presented analysis scenario shows how source reconstruction can be embedded in the EEG classification system and highlights its benefits for brain-computer interfaces (BCI) performance. Results of the analysis, trained classifiers, and selected feature indices can be directly used in BCI feedback training sessions. Linear inverse operators, such as WMNE, and sparse regions-of-interest are computationally simple enough to be applied in real-time settings.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::c5a94eb68a294a5a217249222e71e268Test
https://doi.org/10.1049/pbhe016e_ch7Test

المؤلفون: A. P. Vinod, Kavitha P. Thomas

المصدر: Signal Processing and Machine Learning for Brain-Machine Interfaces ISBN: 9781785613982

مصطلحات موضوعية: InformationSystems_MODELSANDPRINCIPLES, Modality (human–computer interaction), Motor imagery, medicine.diagnostic_test, Human–computer interaction, Computer science, Brain activity and meditation, medicine, Context (language use), Cognitive skill, Electroencephalography, Neurofeedback, Brain–computer interface

الوصف: Brain-computer interface (BCI) is relatively a new approach to communication between man and machine, which translates brain activity into commands for communication and control. As BCI is capable of detecting human intentions, it is a promising communication tool for paralyzed patients for communicating with external world. Many of the current BCI systems employ electroencephalogram (EEG) which is the most widely used noninvasive brain activity recording technique. EEG signal carries potential features to identify and decode human intentions and mental tasks. Recently, many researchers have started exploiting the possibilities of BCI in entertainment and cognitive skill enhancement. BCI-based games have been identified as a unique entertainment mechanism nowadays, “controlling a 2-D, 3-D or virtual computer game solely by player's brain waves.” BCI games work based on a neurofeedback paradigm which allows an individual to self-regulate his brain signal in response to the real-time visual or auditory feedback of his brain waves/features. This neurofeedback in a gaming environment motivates and trains the players to control his brain features toward the desired stage (self-regulation). This chapter explores the state-of-the-art BCI technology in neurofeedback games, employing EEG signal. It also provides a survey of the existing EEG-based neurofeedback games and evaluates their success rates, challenging factors and influence on players. In neurofeedback games, a number of features extracted from EEG accompanied with sustained attention, selective attention, visuospatial attention, motor imagery, eye movements, etc. have been employed as distinct control signals. We will briefly review and compare various signal processing methodologies and machine-learning techniques employed in those studies to extract and decode the brain features. Besides the structure and algorithms used in neurofeedback games, the therapeutic effects of neurofeedback training and its capabilities for the enhancement of cognitive skills will also be briefly discussed in this chapter. Neurofeedback training helps to rewire brain's underlying neural circuits and to improve brain functions. Therefore, it is considered as an effective tool for boosting cognitive skills of both healthy and the disabled. Specifically, neurofeedback has been considered as an efficient treatment modality for individuals with attention-deficit hyperactive disorder (ADHD). ADHD is characterized by three behavioral symptoms: inattention, hyperactivity and impulsivity. Along with the conventional intervention strategies such as medication, behavioral treatments, etc., neurofeedback in BCI games has also been emerging as a promising modality for treating the attention deficit. We will also discuss portable and economical EEG recording devices currently employed in BCI-based brain training modules/games. Finally, the chapter will be concluded with a brief overview of the neurofeedback developments in the context of BCI-based games until now, their potential impact on the healthy as well as on people with neurological disorders, challenges in transferring the successful protocols from laboratories into the market and hurdles in real-time BCI system design and development.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::427bd6165d6ec6b363e7808a586c21c9Test
https://doi.org/10.1049/pbce114e_ch14Test

المؤلفون: Xinyang Li, Huijuan Yang, Cuntai Guan

المصدر: Signal Processing and Machine Learning for Brain-Machine Interfaces ISBN: 9781785613982

مصطلحات موضوعية: Spatial filter, medicine.diagnostic_test, business.industry, Computer science, Binary number, Pattern recognition, Electroencephalography, Signal, ComputingMethodologies_PATTERNRECOGNITION, Motor imagery, Discriminative model, medicine, Artificial intelligence, business, Image resolution, Brain–computer interface

الوصف: Different mental states result in different synchronizations or desynchronizations between multiple brain regions, and subsequently, electroencephalogram (EEG) connectivity analysis gains increasing attention in brain computer interfaces (BCIs). Conventional connectivity analysis is usually conducted at the scalp-level and in an unsupervised manner. However, due to the volume conduction effect, EEG data suffer from low signal-to-noise ratio and poor spatial resolution. Thus, it is hard to effectively identify the task-related connectivity pattern at the scalp-level using unsupervised method. There exist extensive discriminative spatial filtering methods for different BCI paradigms. However, in conventional spatial filter optimization methods, signal correlations or connectivities are not taken into consideration in the objective functions. To address the issue, in this work, we propose a discriminative connectivity pattern-learning method. In the proposed framework, EEG correlations are used as the features, with which Fisher's ratio objective function is adopted to optimize spatial filters. The proposed method is evaluated with a binary motor imagery EEG dataset. Experimental results show that more connectivity information are maintained with the proposed method, and classification accuracies yielded by the proposed method are comparable to conventional discriminative spatial filtering method.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::cd5be2f7f251507657166b3a611faa7eTest
https://doi.org/10.1049/pbce114e_ch2Test

المؤلفون: Uswah Khairuddin, Hilman Fauzi, Zool Hilmi Ismail, Mohd Ibrahim Shapiai, S. Shah

المصدر: Scopus-Elsevier

مصطلحات موضوعية: medicine.diagnostic_test, Computer science, business.industry, Feature extraction, Pattern recognition, Electroencephalography, Motor imagery, medicine, Leverage (statistics), Artificial intelligence, Performance improvement, business, Brain–computer interface, Extreme learning machine, Communication channel

الوصف: Motor imagery on EEG signals are widely used in brain computer interface (BCI) system with many interesting applications. However, it is not easy to interpret motor imagery EEG signal due to non-stationary and noisy features of the signal. In this paper, we investigate three different techniques of energy calculation as a part of energy extraction methods including L2-norm, leverage score, and absolute Z-score. This BCI framework use CSP as motor imagery signal feature extraction method and extreme learning machine (ELM) to classify the features of motor imagery signal. In general, the investigated framework has proved that the energy extraction methods can improve the performance of CSP. Also, an effective EEG channel selection provides better performance in terms of classification accuracy. In general, the proposed energy extraction methods can offer up to 21% performance improvement in accuracy and 86% reduction number of channels as compared to the original CSP.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5d5f4085e7138494118c2e6d937a227Test
https://doi.org/10.1049/cp.2018.1599Test