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4th International Conference on Systems and Informatics (ICSAI)

作者： Martinez-Maradiaga, Daniel Meixner, Gerrit Heilbronn Univ UniTyLab Heilbronn Germany

ISBN: (纸本)9781538611074

Studies show that drowsiness while driving a motor vehicle can result in fatal car accidents. A smartphone application that uses a low-cost EEG brain-computer-interface is described. Morpheus Alert monitors both the brain waves ratio index (Alpha + Theta) / Beta and the user's attention level to detect drowsiness and prevent microsleeps. Two scenarios were created to record EEG data and analyze the differences of the ratio index and attention levels in normal and drowsy conditions. Data showed that the ratio index increases in average 29% in drowsy conditions. The application uses the NeuroSky MindWave Mobile EEG headset to obtain the user's attention levels as well as the alpha, beta and theta brainwaves magnitude values;the application constantly monitor the user's state and makes the smartphone vibrate and play a loud sound when a drowsy state is detected to alert the user. Results show that low attention levels in combination with high magnitude values of alpha and theta brain waves correlate to drowsiness. Morpheus Alert was able to correctly detect drowsiness in 73% of the cases.

关键词： brain-computer-interface Microsleep Android Application NeuroSky

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Open-source concealed EEG data collection for brain-computer-interfaces-neural observation through OpenBCI amplifiers with around-the-ear cEEGrid electrodes

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brain-computer interfaceS 2021年第4期8卷 161-179页

作者： Knierim, Michael Thomas Berger, Christoph Reali, Pierluigi Karlsruhe Inst Technol KIT Inst Informat Syst & Mkt IISM Karlsruhe Germany Politecn Milan Dept Elect Informat & Bioengn Milan Italy

Observing brain activity in real life offers exciting possibilities like the support of physical health, mental well-being, and thought-controlled interaction modalities. The development of such applications is, however, strongly impeded by low accessibility to research-grade neural data and a lack of easy-to-use and comfortable sensors. To overcome these challenges, we present the cost-effective adaptation of concealed around-the-ear EEG electrodes (cEEGrids) to the open-source OpenBCI EEG signal acquisition platform. The system combines publicly available electronic components with 3D-printed parts, forming an easily replicable recording system for prolonged use and flexible application development. To demonstrate the system's feasibility, experimentally induced changes in visual stimulation and mental workload are replicated. Lastly, a novel application area is investigated, the observation of flow experiences through observation of temporal Alpha power changes. Support for a link between temporal Alpha power and flow is found, indicating an efficient engagement of verbal-analytic reasoning with intensified flow levels.

关键词： Concealed EEG OpenBCI cEEGrid brain-computer-interface wearables

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Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition

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computer METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年第12期27卷 1649-1663页

作者： Kheirabadi, Raoufeh Omranpour, Hesam Babol Noshirvani Univ Technol Dept Elect & Comp Engn Babol Iran

Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.

关键词： Human emotion recognition EMD brain-computer-interface LASSO EEG

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EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms

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JOURNAL OF NEURAL ENGINEERING 2024年第3期21卷 036020-036020页

作者： Wimpff, Martin Gizzi, Leonardo Zerfowski, Jan Yang, Bin Univ Stuttgart Inst Signal Proc & Syst Theory Stuttgart Germany Fraunhofer Inst Mfg Engn & Automat IPA Stuttgart Germany Charite Univ Med Berlin Clin Neurotechnol Lab Dept Psychiat & Neurosci Charite Campus Mitte CCM Berlin Germany

Objective. The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. Approach. We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Results. Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Significance. Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.

关键词： deep learning attention brain-computer-interface motor imagery EEG decoding

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A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

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IEEE ACCESS 2023年 11卷 118849-118864页

作者： Penava, Pascal Buettner, Ricardo Univ Bayreuth Chair Informat Syst & Data Sci BWL XVIII D-95447 Bayreuth Germany

We demonstrate how to use generative adversarial networks to improve the small data problem when training brain-computer-interfaces. The new approach is based on finely graded frequency bands, which are extracted from motor imagery electroencephalography data by using power spectral density method to synthetically generate electroencephalography data using generative adversarial networks. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets, by first checking the synthetic and real data for statistical and visual similarity, and secondly, by training a random forest classifier, once using only the real data and then using the real data augmented with the synthetic data. With similarity scores of 95.72 % in the subject-dependent case and 83.51 % in the subject-independent case, and a predictive gain of 17.53 % in the subject-dependent case, and 7.51 % in the subject-independent case, we were able to achieve promising results. The results show that our approach can make it possible to research rare diseases for which there is too little patient data. Also, synthetic data can be a way for many electroencephalography-based brain-computer interface applications to obtain the required data more cost- and time-efficiently.

关键词： brain-computer-interface decision prediction generative adversarial networks motor imagery tasks electroencephalography machine learning

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EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Toward Large-Scale EEG Model

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1653-1663页

作者： Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Peoples R China Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool L69 3BX England Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Xian Jiaotong Liverpool Univ Design Sch Suzhou 215123 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England

The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of "EEG Parser", "Correction", "Batch Processing", and "Large Language Model Boost". Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at ***/Baizhige/EEGUnity.

关键词： Electroencephalography Data processing Batch production systems brain modeling Hands Cleaning Annotations Large language models Standards Proposals brain-computer-interface electroencephalogram data integration large-scale model open-source software

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Masked EEG Modeling for Driving Intention Prediction

Masked EEG Modeling for Driving Intention Prediction

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhou, Jinzhao Sia, Justin Duan, Yiqun Chang, Yu-Cheng Wang, Yu-Kai Lin, Chin-Teng Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

ISBN: (纸本)9798350359329;9798350359312

Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Recent endeavors to prevent driving accidents have focused on using electroencephalography (EEG) to identify drowsy mental states. However, a more versatile EEG-based assistant system is better equipped to achieve smooth human-machine interaction in driving scenarios. This system should be capable of understanding a driver's intention while demonstrating resilience to artifacts induced by unexpected sudden movements. Building on this goal, this paper pioneers a novel research direction in the field of brain-computer interface for assisted driving, studying the neural patterns associated with driving intentions and presenting a novel method for driving intention prediction. In particular, our preliminary analysis of the EEG signal using independent component analysis suggests a close relation between the intention of driving maneuvers and the neural activities in central-frontal and parietal areas. Power spectral density analysis at a group level also reveals a notable distinction among various driving intentions in the frequency domain. To exploit these brain dynamics, we propose a novel Masked EEG Modeling (MEM) framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Extensive experiments, encompassing comprehensive quantitative and qualitative assessments on a publicly available driving dataset, demonstrate the proposed MEM method is proficient in predicting driving intentions across various vigilance states. Specifically, our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving. Ablation also demonstrates that our method significantly enhances the flexibility in handling missing channels. Notably, our method maintains over 75% accuracy when more than half of the channe

关键词： Electroencephalography Assisted Driving brain-computer-interface Human-Machine Interaction Driving Intention Prediction

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Ethical Challenges Associated with the Development and Deployment of brain computer interface Technology

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NEUROETHICS 2014年第2期7卷 109-122页

作者： McCullagh, Paul Lightbody, Gaye Zygierewicz, Jaroslaw Kernohan, W. George Univ Ulster Sch Comp & Math Newtownabbey BT37 0QB North Ireland Uniwersytet Warszawski Wydzial Fizyki PL-00681 Warsaw Poland Univ Ulster Sch Nursing Newtownabbey BT37 0QB Antrim North Ireland

brain computer interface (BCI) technology offers potential for human augmentation in areas ranging from communication to home automation, leisure and gaming. This paper addresses ethical challenges associated with the wider scale deployment of BCI as an assistive technology by documenting issues associated with the development of non-invasive BCI technology. Laboratory testing is normally carried out with volunteers but further testing with subjects, who may be in vulnerable groups is often needed to improve system operation. BCI development is technically complex, sometimes requiring lengthy recording sessions to achieve the necessary personalisation of the paradigms, and this can present ethical challenges that vary depending on the subject group. The paper contributes to the on-going ethical discussion surrounding the deployment BCI outside the specialist laboratory and suggests some tentative guidelines for BCI research teams, appropriate to those deploying the technology, derived from experience on a multisite project. Any tension between deployment and technical progress must be managed by a formal process within a multidisciplinary consortium.

关键词： Neuroethics brain-computer-interface Non-invasive Development Deployment

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brain-computer interface after Nervous System Injury

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NEUROSCIENTIST 2014年第6期20卷 639-651页

作者： Burns, Alexis Adeli, Hojjat Buford, John A. Ohio State Univ Biomed Engn Grad Program Columbus OH 43210 USA Ohio State Univ Dept Biomed Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Civil & Environm Engn & Geod Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn & Neurosci Columbus OH 43210 USA Ohio State Univ Biophys Grad Program Columbus OH 43210 USA Ohio State Univ Phys Therapy Div Sch Hlth & Rehabil Sci Columbus OH 43210 USA

brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.

关键词： computational neuroscience signal processing wavelets Parkinson's disease brain-computer-interface

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A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform

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HARDWAREX 2022年 12卷 e00357页

作者： Knierim, Michael T. Schemmer, Max Bauer, Niklas Karlsruhe Inst Technol KIT Inst Informat Syst & Mkt IISM Kaiserstr 89-93 D-76133 Karlsruhe Germany

We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assem-bly. Besides describing how to replicate and use the system, we highlight promising appli-cation scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common mea-surement artefacts and methods for removing them are provided, introducing a prototyp-ical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system's capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the sys-tem could, for instance, be used to devise applications that reduce pathological jaw clench-ing and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing sys-tems and a low-cost alternative to current commercial alternatives. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： OpenBCI cEEGrid brain-computer-interface Wearables

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