Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models by Sevgi Gokce Aslan, Bulent Yilmaz

“…The findings of this study may provide significant contributions to the development of effective methods for the rehabilitation and treatment of swallowing difficulties based on motor imagery-based brain computer interfaces.…”
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A bibliometric analysis of studies on artificial intelligence in neuroscience by Ugur Tekin, Murat Dener, Murat Dener

“…The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. …”
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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

“…In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.…”
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A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach by Parisa Gazerani

“…Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.ConclusionA closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. …”
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Estado del Arte en Neurotecnologías para la Asistencia y la Rehabilitación en España: Tecnologías Fundamentales by Luis J. Barrios, Roberto Hornero, Javier Pérez-Turiel, José L. Pons, Joan Vidal, José M. Azorín

“…Palabras clave: Neurotecnologías, interfaces cerebro-computador, robótica, procesamiento de señal, estimulación eléctrica, sistemas biomédicos, rehabilitación, tecnologías de asistencia, Keywords: Neurotechnologies, Brain-Computer Interfaces, Robotics, Signal Processing, Electrical Stimulation, Biomedical Systems, Rehabilitation, Assistive Technologies…”
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Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature by Pragati Patel, Sivarenjani B., Ramesh Naidu Annavarapu

“…Abstract Emotion identification acts as a critical component in passive brain-computer interfaces. The domain of EEG-based emotion identification has garnered substantial attention owing to advancements in machine learning models, notably in terms of higher accuracy and broader generalization capabilities. …”
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Neuroeducation: understanding neural dynamics in learning and teaching by K. Pradeep, Rajalakshmi Sulur Anbalagan, Asha Priya Thangavelu, S. Aswathy, V. G. Jisha, V. S. Vaisakhi

“…Furthermore, the integration of technology into educational practices, ranging from brain-computer interfaces to immersive virtual reality experiences, presents new possibilities for enhancing learning engagements and accommodating diverse learning styles and curricula. …”
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Microbial biotechnology alchemy: Transforming bacterial cellulose into sensing disease- A review by Ali Jawad Akki, Pratheek Jain, Ravindra Kulkarni, Raghavendra Rao Badkillaya, Raghavendra V. Kulkarni, Farhan Zameer, V Raghu Anjanapura, Tejraj M. Aminabhavi

“…The review covers various bacterial cellulose (BC)-based biosensors, from SARS-CoV-2 detection to wearable health monitoring and interaction with human-computer interfaces. BC's integration into ionic thermoelectric hydrogels for wearable health monitoring shows its potential for real-time health tracking. …”
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Computational Brain Imaging Framework for Neurological Mapping and Disorder Classification Using Multimodal Image Processing by S. Karthikeyan, B. Muthu Kumar, M. L. Kiran, K. Srivatsan

“…The MN-CICT is a revolutionary method to brain imaging, which paves the way for the development of novel applications in the fields of brain–computer interfaces, customized medicine, and automated diagnostics.…”
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Tactile imagery affects cortical responses to vibrotactile stimulation of the fingertip by Marina Morozova, Lev Yakovlev, Nikolay Syrov, Mikhail Lebedev, Alexander Kaplan

“…We propose incorporating TI in imagery-based brain-computer interfaces (BCIs) to enhance sensorimotor restoration and sensory substitution. …”
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MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification by Ling-Long Li, Guang-Zhong Cao, Yue-Peng Zhang, Wan-Chen Li, Fang Cui

“…Decoding lower-limb motor imagery (MI) is highly important in brain–computer interfaces (BCIs) and rehabilitation engineering. …”
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MT-EfficientNetV2: A Multi-Temporal Scale Fusion EEG Emotion Recognition Method Based on Recurrence Plots by Zihan Zhang, Zhiyong Zhou, Jun Wang, Hao Hu, Jing Zhao

“…Emotion recognition based on electroencephalography (EEG) signals has garnered significant research attention in recent years due to its potential applications in affective computing and brain-computer interfaces. Despite the proposal of various deep learning-based methods for extracting emotional features from EEG signals, most existing models struggle to effectively capture both long-term and short-term dependencies within the signals, failing to fully integrate features across different temporal scales. …”
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Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification by Marcos Loaiza-Arias, Andrés Marino Álvarez-Meza, David Cárdenas-Peña, Álvaro Ángel Orozco-Gutierrez, German Castellanos-Dominguez

“…Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. …”
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Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasksMendeley Data by Ali Nirabi, Faridah Abd Rahman, Mohamed Hadi Habaebi, Khairul Azami Sidek, Siti Yusoff

“…The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress.The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. …”
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A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP by Hongqi Li, Yujuan Wang, Peirong Fu

“…Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. …”
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EEG channels selection for stroke patients rehabilitation using equilibrium optimizer by Al-Betar Mohammed Azmi, Alyasseri Zaid Abdi Alkareem, Makhadmeh Sharif Naser

“…Researchers have proposed various applications to assist in the rehabilitation of stroke patients, with brain-computer interfaces (BCIs) utilizing electroencephalograms (EEGs) showing particularly promising outcomes. …”
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MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition by Qi Mao, Hongke Zhu, Wenyao Yan, Yu Zhao, Xinhong Hei, Jing Luo

“…<b>Background</b>: In brain–computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. …”
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Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems by Maham Nayab, Asim Waris, Muhammad Jawad Khan, Dokhyl AlQahtani, Ahmed Imran, Syed Omer Gilani, Umer Hameed Shah

“…Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. …”
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Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients by Jin Feng, YunDe Li, ZiJun Huang, Yehang Chen, SenLiang Lu, RongLiang Hu, QingHui Hu, YuYao Chen, XiMiao Wang, Yong Fan, Jing He

“…CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.…”
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Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals by Delshad Ghavami, Moein Radman, Ali Chaibakhsh

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