☆ 4.6 Review

Functional Mapping of the Brain for Brain-Computer Interfacing: A Review

ELECTRONICS (2023)

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A hybrid neuro-experimental decision support system to classify overconfidence and performance in a simulated bubble using a passive BCI

Filip-Mihai Toma

Summary: Significant advancements in brain-computer interfaces (BCIs) can lead to the development of enhanced decision-making platforms. Experimental approaches using neuroimaging equipment and artificial intelligence can improve understanding and predict behavior in scenarios like asset bubbles. This study presents a hybrid classification methodology using EEG-based experiments and machine learning classifiers to classify subjects based on overconfidence and trading performance, achieving high accuracy rates of 80-85%.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

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Article Engineering, Biomedical

Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

Tian -Wei Shi et al.

Summary: This paper proposes a brain computer interface (BCI) system that integrates monocular vision navigation and decision subsystems to enable low-speed UAVs to search for indoor 3D space targets. The monocular vision navigation subsystem uses SIFT and BF algorithms to extract and match key points, estimating obstacles and their positions. The decision subsystem collects motor imaginary (MI) tasks EEG signals and applies preprocessing and filtering techniques, utilizing CNN for feature extraction, classification, and flight direction decision. Experimental results demonstrate the adaptability and control stability of the BCI system.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2023)

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Article Engineering, Biomedical

EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network

Mei-yu Zhong et al.

Summary: This paper proposes a novel EEG-based emotion recognition framework using TQWT-features and HCRNN model to extract emotional information from multichannel EEG signals. The experimental results on the SEED dataset demonstrate its effectiveness.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2023)

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Article Engineering, Biomedical

Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model

Rahul Sharma et al.

Summary: This study compares traditional classification methods with Deep Learning techniques in BCI applications, highlighting the importance of Multi-Layered Perceptron. Support Vector Machine (SVM) showed the shortest training time and prediction speed with comparable accuracy among traditional ML-based algorithms, while the subject-independent generalized MLP model achieved approximately 90% accuracy with half the classification time compared to traditional models. The results suggest that a standard MLP model can serve as a basis for advanced subject-independent BCI with further optimization.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

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Article Engineering, Biomedical

FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces

Youngchul Kwak et al.

Summary: This study proposed a deep learning-based early fusion structure, known as the fNIRS-guided attention network (FGANet), to overcome performance issues in hybrid EEG-fNIRS BCI systems. Experimental results showed that FGANet outperformed state-of-the-art algorithms by 4.0% and 2.7% in mental arithmetic and motor imagery tasks, respectively.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2022)

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Article Neurosciences

Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study

Xiaodong Li et al.

Summary: This study used real-time functional magnetic resonance imaging neurofeedback to train patients with chronic insomnia disorder to regulate their amygdala activity, resulting in improved sleep and altered functional networks. These findings contribute to our understanding of the mechanism of real-time functional magnetic resonance imaging neurofeedback in insomnia treatment.

FRONTIERS IN MOLECULAR NEUROSCIENCE (2022)

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Review Neurosciences

2020 International brain-computer interface competition: A review

Ji-Hoon Jeong et al.

Summary: The brain-computer interface (BCI) is a communication tool that connects the brain with external devices, and has applications beyond communication and control. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for evaluation of BCI advancements. Challenges like few-shot EEG learning, micro-sleep detection, imagined speech decoding, cross-session classification, and ambulatory EEG detection were discussed. The competition attracted participation from scientists and scholars with diverse backgrounds and nationalities, leading to notable BCI advancements and identification of trends for BCI researchers.

FRONTIERS IN HUMAN NEUROSCIENCE (2022)

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Article Computer Science, Artificial Intelligence

Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces

Biao Sun et al.

Summary: This paper proposes a deep learning-based method to improve the accuracy of brain computer interfaces (BCIs) by transferring the data distribution from golden subjects to BCI-illiterate users. By aligning the dimensionality-reduced BCI-illiterate data with the data of golden subjects using a generator and a CNN classifier, this method outperforms traditional classification methods in terms of accuracy and robustness. The results demonstrate the effectiveness of this approach in identifying EEG signals and its robustness to inter-subject variations.

NEURAL NETWORKS (2022)

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Article Computer Science, Information Systems

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Xiaolin Liu et al.

Summary: Brain-computer interface (BCI) is a promising technology that can improve human living quality. This paper proposes a temporal and channel attention convolutional network (TCACNet) for motor imagery electroencephalogram (MI-EEG) classification, which achieves accurate classification decisions by focusing on task-related time slices and electrode channels.

INFORMATION PROCESSING & MANAGEMENT (2022)

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Article Engineering, Biomedical

On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery

Hao Zhu et al.

Summary: Motor imagery based brain-computer interface is an important BCI paradigm that requires powerful classifiers. Deep learning technology has led to the development of multiple classification models, and finding the best performing model is crucial for designing better BCI systems. However, comparing the performance of these models directly is difficult due to differing datasets. In this study, five deep classification models were tested on two large, publicly available databases, with EEGNet performing the best on one database.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2022)

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Article Computer Science, Theory & Methods

Security in Brain-Computer Interfaces: State-of-the-Art, Opportunities, and Future Challenges

Sergio Lopez Bernal et al.

Summary: This article introduces the important role of Brain-Computer Interfaces (BCIs) in improving the quality of life of patients and points out the future trends of BCI development. It also discusses the potential harm of security attacks on BCIs and corresponding countermeasures.

ACM COMPUTING SURVEYS (2021)

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Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors

Satya P. Singh et al.

Summary: This study introduces a deep neural network architecture for accurate decoding of human activity from multiple sensors, which selects and learns important time points using a self-attention mechanism. Results demonstrate significant performance improvement over previous deep networks and state-of-the-art methods.

IEEE SENSORS JOURNAL (2021)

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Article Neurosciences

MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer

Anastasia O. Ovchinnikova et al.

Summary: Researchers demonstrate the potential of discriminating voluntary and spontaneous eye fixations using segments of MEG data. Applying CNN for binary classification of MEG signals related to eye fixations shows promising results in distinguishing voluntary from spontaneous fixations, supporting the improvement of gaze-based interfaces.

FRONTIERS IN NEUROSCIENCE (2021)

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Article Computer Science, Artificial Intelligence

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Mamunur Rashid et al.

Summary: Brain-computer interface (BCI) is an alternative communication strategy for patients of neurological disorders, which relies on the efficacy of feature extraction and selection techniques as well as classification algorithms. In this study, an ensemble learning-based approach showed superior classification accuracy in motor imagery (MI) data.

PEERJ COMPUTER SCIENCE (2021)

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Article Biology

Practical real-time MEG-based neural interfacing with optically pumped magnetometers

Benjamin Wittevrongel et al.

Summary: OPM-MEG demonstrates robust single-trial analysis in real-time 'mind-spelling' applications with an average accuracy of 97.7%. This indicates the potential of OPM-MEG to effectively utilize neuro-magnetic brain responses in a practical and flexible manner, paving the way for a wide range of new neural interface applications in the future.

BMC BIOLOGY (2021)

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Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors

Satya P. Singh et al.

Summary: The paper proposes a method for normalizing 3D volumetric scans to aid CNN in more accurate detection of abnormalities. Compared with other methods, it shows significant improvement in classification performance, especially for detecting different acute brain hemorrhages effectively. However, the proposed method has limitations in detecting varied abnormalities.

IEEE SENSORS JOURNAL (2021)

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EEG classification using sparse Bayesian extreme learning machine for brain-computer interface

Zhichao Jin et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Alexander E. Hramov et al.

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3D Deep Learning on Medical Images: A Review

Satya P. Singh et al.

SENSORS (2020)

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Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

Nong Yan et al.

IEEE ACCESS (2020)

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Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface

Marie-Constance Corsi et al.

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (2019)

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Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD

K. Rubia et al.

NEUROIMAGE (2019)

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BCI2000Web and WebFM: Browser-Based Tools for Brain Computer Interfaces and Functional Brain Mapping

Grifffin Milsap et al.

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Towards neural co-processors for the brain: combining decoding and encoding in brain-computer interfaces

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CURRENT OPINION IN NEUROBIOLOGY (2019)

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Article Chemistry, Analytical

Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans

Justin Ker et al.

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Emotion Regulation of Hippocampus Using Real-Time fMRI Neurofeedback in Healthy Human

Yashuo Zhu et al.

FRONTIERS IN HUMAN NEUROSCIENCE (2019)

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The orbitofrontal cortex processes neurofeedback failure signals

Christian Paret et al.

BEHAVIOURAL BRAIN RESEARCH (2019)

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An Automatic Subject Specific Intrinsic Mode Function Selection for Enhancing Two-Class EEG-Based Motor Imagery-Brain Computer Interface

Pramod Gaur et al.

IEEE SENSORS JOURNAL (2019)

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Bor-Shyh Lin et al.

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IEEE ACCESS (2019)

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2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) (2019)

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IEEE TRANSACTIONS ON FUZZY SYSTEMS (2018)

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A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback

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Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification

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Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain-Computer Interface

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2017)

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Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG

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Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface

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