Article
Neurosciences
Yiwei Wang, Ling You, Kamun Tan, Meijie Li, Jingshan Zou, Zhifeng Zhao, Wenxin Hu, Tianyu Li, Fenghua Xie, Caiqin Li, Ruizhi Yuan, Kai Ding, Lingwei Cao, Fengyuan Xin, Congping Shang, Miaomiao Liu, Yixiao Gao, Liqiang Wei, Zhiwei You, Xiaorong Gao, Wei Xiong, Peng Cao, Minmin Luo, Feng Chen, Kun Li, Jiamin Wu, Bo Hong, Kexin Yuan
Summary: This study explores the role of the medial sector of the auditory thalamus (ATm) in controlling general and defensive arousal in mice. The activity of ATm(VGluT2+) neurons is correlated with wakefulness and contributes to sensory-induced arousal. Inhibiting ATm(VGluT2+) neurons or downstream pathways reduces the likelihood of sensory-induced arousal in sleeping mice. Activation of ATm(VGluT2+) leads to heightened arousal, accompanied by anxiety and avoidance behavior in awake mice. Blocking neurotransmission in ATm(VGluT2+) neurons eliminates alerting stimuli-induced defensive behaviors. These findings may have implications for understanding sleep disturbances and abnormal sensory sensitivity in specific brain disorders.
Article
Multidisciplinary Sciences
Rabia Avais Khan, Nasir Rashid, Muhammad Shahzaib, Umar Farooq Malik, Arshia Arif, Javaid Iqbal, Mubasher Saleem, Umar Shahbaz Khan, Mohsin Tiwana
Summary: Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. In this study, a novel framework for classifying binary-class electroencephalogram (EEG) data has been proposed. The framework achieved high classification accuracies with logistic regression classifier on two datasets, indicating its potential for real-time Brain-Computer Interface (BCI) systems and 2-class Motor Imagery signals classification applications.
Article
Neurosciences
Gan Huang, Zhenxing Hu, Weize Chen, Shaorong Zhang, Zhen Liang, Linling Li, Li Zhang, Zhiguo Zhang
Summary: EEG signals exhibit commonality and variability across subjects, sessions, and tasks. This study introduces an EEG-based biometric competition based on a large-scale M3CV database to promote the development of machine learning algorithms and achieve a better understanding of the commonality and variability of EEG signals.
Article
Computer Science, Artificial Intelligence
Vasilisa Mishuhina, Xudong Jiang
Summary: The novel approach of TFCSP enhances the robustness and accuracy of EEG signal classification, outperforming state-of-the-art methods. Adopting subject reaction time paradigm is useful to enhance classification performance, and using complex CSP in the frequency domain is significantly effective compared to commonly used bandpass filters in the time domain.
PATTERN RECOGNITION
(2021)
Review
Engineering, Biomedical
Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David McAlpine, Yu Zhang
Summary: Brain signals are biometric information collected from the human brain for decoding the underlying neurological or physical status of individuals. Recent advancements in deep learning have significantly enhanced the study of brain signals. This work presents a taxonomy of non-invasive brain signals, basics of deep learning algorithms, frontiers of applying deep learning for non-invasive brain signals analysis, and potential real-world applications.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Engineering, Biomedical
Jingfeng Bi, Ming Chu
Summary: The goal of this study is to design a single-limb, multi-category motor imagery paradigm and achieve cross-subject intention recognition through the transfer data learning network (TDLNet). The network processes cross-subject EEG signals and assigns weights to signal channels using the Residual Attention Mechanism Module (RAMM), resulting in the best classification results.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Cedric Rommel, Joseph Paillard, Thomas Moreau, Alexandre Gramfort
Summary: This study validates existing EEG data augmentation approaches through a unified and exhaustive analysis. The results demonstrate that employing appropriate data augmentations can significantly improve the accuracy of EEG classification tasks in low data regimes. Furthermore, the study shows that there is no single best augmentation strategy for different tasks.
JOURNAL OF NEURAL ENGINEERING
(2022)
Review
Health Care Sciences & Services
Yixin Ma, Anmin Gong, Wenya Nan, Peng Ding, Fan Wang, Yunfa Fu
Summary: Brain-computer interfaces (BCIs) are a new technology that revolutionizes traditional human-computer interaction by directly accessing control signals from users' brains. However, the individual differences in physiology, mental states, perception, cognition, and brain functions make it necessary to customize personalized BCIs for specific users. This study focuses on defining personalized BCIs, detailing their design, development, evaluation methods, and applications, as well as discussing the challenges and future directions. It is expected to provide useful insights for innovative research and practical applications of personalized BCIs.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Mathematical & Computational Biology
Xu Yin, Ming Meng, Qingshan She, Yunyuan Gao, Zhizeng Luo
Summary: A new optimal channel-based sparse time-frequency blocks common spatial pattern (OCSB-CSP) feature extraction method is proposed to improve the classification accuracy and computational efficiency of the model. Comparative experiments on two public EEG datasets show that the proposed method outperforms other winner methods in terms of classification performance. This provides a new idea for enhancing BCI applications.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Wei Xiong, Qingguo Wei
Summary: This paper proposes a novel approach to reduce the calibration time of MI-based BCIs without sacrificing classification accuracy by generating artificial EEG data and expanding the training set size. The proposed algorithm significantly reduces the amount of training data needed to achieve a given performance level.
Article
Computer Science, Artificial Intelligence
Binghua Li, Zhiwen Zhang, Feng Duan, Zhenglu Yang, Qibin Zhao, Zhe Sun, Jordi Sole-Casals
Summary: This study introduces a component-mixing strategy (CMS) for motor imagery (MI) data augmentation, which extends empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition. CMS can generate artificial trials from a few training samples without required training and has been shown to significantly improve binary classification accuracy and area under the curve scores using different algorithms on the BCI Competition IV dataset 2b.
Article
Computer Science, Artificial Intelligence
Kahoko Takahashi, Zhe Sun, Jordi Sole-Casals, Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Hui-Hai Zhao, Shangkun Deng, Ruggero Micheletto
Summary: This study proposed a method of generating artificial data using empirical mode decomposition (EMD) to train neural networks for brain computer interfaces. The experiments showed that introducing artificial frames significantly improved performance and reduced the number of experiments and training costs.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Mohamed Ezzat, Mohamed Maged, Youssef Gamal, Mustafa Adel, Mohammed Alrahmawy, Sara El-Metwally
Summary: Eye-based communication languages, such as Blink-To-Speak, are vital for patients with motor neuron disorders to express their needs and emotions. Blink-To-Live is an eye-tracking system that utilizes a modified Blink-To-Speak language and computer vision for patients with speech impairments. The system tracks the patient's eyes using a mobile phone camera, and utilizes computer vision modules for facial landmarks detection, eye identification, and tracking.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Interdisciplinary Applications
Jing Sun, Mengting Wei, Ning Luo, Zhanli Li, Haixian Wang
Summary: Common spatial patterns (CSP) is a widely used method for feature extraction of EEG signals. This study proposes Euler CSP (e-CSP) as a feature extraction technique for EEG signals, by applying CSP in the Euler space. Experimental results demonstrate the discriminative ability of e-CSP.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Neurosciences
Sai Kalyan Ranga Singanamalla, Chin-Teng Lin
Summary: The performance of brain-computer interfaces (BCIs) has improved significantly with advanced machine learning methods, but the generation of synthetic EEG signals remains a challenge. A generative model is needed to efficiently produce multi-class artificial EEG samples with as few original trials as possible while retaining the biomarker of the signal.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Behavioral Sciences
Sophie Metz, Sabine Aust, Yan Fan, Luisa Boenke, Zjala Harki, Matti Gaertner, Malek Bajbouj, Simone Grimm
BEHAVIOURAL BRAIN RESEARCH
(2018)
Article
Neurosciences
Matti Gaertner, M. Elisabetta Ghisu, Milan Scheidegger, Luisa Boenke, Yan Fan, Anna Stippl, Ana-Lucia Herrera-Melendez, Sophie Metz, Emilia Winnebeck, Maria Fissler, Anke Henning, Malek Bajbouj, Karsten Borgwardt, Thorsten Barnhofer, Simone Grimm
NEUROPSYCHOPHARMACOLOGY
(2018)
Article
Endocrinology & Metabolism
Corinna Hartling, Yan Fan, Anne Weigand, Irene Trilla, Matti Gaertner, Malek Bajbouj, Isabel Dziobek, Simone Grimm
PSYCHONEUROENDOCRINOLOGY
(2019)
Article
Clinical Neurology
Matti Gaertner, Sabine Aust, Malek Bajbouj, Yan Fan, Katja Wingenfeld, Christian Otte, Isabella Heuser-Collier, Heinz Boeker, Josef Haettenschwiler, Erich Seifritz, Simone Grimm, Milan Scheidegger
EUROPEAN NEUROPSYCHOPHARMACOLOGY
(2019)
Article
Clinical Neurology
Sabine Aust, Matti Gaertner, Laura Basso, Christian Otte, Katja Wingenfeld, Woo Ri Chae, Isabella Heuser-Collier, Francesca Regen, Nicoleta Carmen Cosma, Franziska van Hall, Simone Grimm, Malek Bajbouj
EUROPEAN NEUROPSYCHOPHARMACOLOGY
(2019)
Letter
Psychiatry
Michael Lifshitz, Matthew D. Sacchet, Julia M. Huntenburg, Thomas Thiery, Yan Fan, Matti Gaertner, Simone Grimm, Emilia Winnebeck, Maria Fissler, Titus A. Schroeter, Daniel S. Margulies, Thorsten Barnhofer
PSYCHOTHERAPY AND PSYCHOSOMATICS
(2019)
Article
Psychiatry
Laura Basso, Luisa Boenke, Sabine Aust, Matti Gaertner, Isabella Heuser-Collier, Christian Otte, Katja Wingenfeld, Malek Bajbouj, Simone Grimm
JOURNAL OF PSYCHIATRIC RESEARCH
(2020)
Correction
Psychiatry
Laura Basso, Luisa Boenke, Sabine Aust, Matti Gaertner, Isabella Heuser-Collier, Christian Otte, Katja Wingenfeld, Malek Bajbouj, Simone Grimm
JOURNAL OF PSYCHIATRIC RESEARCH
(2020)
Article
Clinical Neurology
Ana Herrera-Melendez, Anna Stippl, Sabine Aust, Milan Scheidegger, Erich Seifritz, Isabella Heuser-Collier, Christian Otte, Malek Bajbouj, Simone Grimm, Matti Gaertner
Summary: This study investigated the impact of ketamine on rapid symptom improvement in depressive patients and found that patients with a larger baseline volume of the bilateral rostral anterior cingulate cortex were more likely to experience rapid symptom reduction. No volumetric changes were observed 24 hours post-treatment.
EUROPEAN NEUROPSYCHOPHARMACOLOGY
(2021)
Article
Psychiatry
A. Stippl, M. Scheidegger, S. Aust, A. Herrera, M. Bajbouj, M. Gaertner, S. Grimm
Summary: The study examined the specific symptom responses in MDD patients after ketamine treatment and identified brain activity associated with different symptoms. Results showed that ketamine has differential effects on MDD symptoms, with the most significant reduction in cognitive symptoms.
JOURNAL OF PSYCHIATRIC RESEARCH
(2021)
Article
Clinical Neurology
Luisa Carstens, Corinna Hartling, Anna Stippl, Ann-Kathrin Domke, Ana Lucia Herrera-Mendelez, Sabine Aust, Matti Gaertner, Malek Bajbouj, Simone Grimm
Summary: This study found that single item scores from the MADRS were strong predictors of ECT outcomes, particularly affecting symptoms. A stronger reduction in affective symptoms during ECT was positively associated with better treatment outcomes. These findings support the benefits of a symptom-based approach in depression research and treatment.
EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE
(2021)
Article
Neurosciences
Viola Borchardt, Yan Fan, Marie Dietz, Ana Lucia Herrera Melendez, Malek Bajbouj, Matti Gaertner, Meng Li, Martin Walter, Simone Grimm
Article
Psychology, Clinical
Emilia Winnebeck, Maria Fissler, Matti Gaertner, Paul Chadwick, Thorsten Barnhofer
BEHAVIOUR RESEARCH AND THERAPY
(2017)
Article
Behavioral Sciences
Maria Fissler, Emilia Winnebeck, Titus A. Schroeter, Marie Gummbersbach, Julia M. Huntenburg, Matti Gaertner, Thorsten Barnhofer
COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE
(2017)
Article
Geriatrics & Gerontology
Matti Gaertner, Simone Grimm, Sabine Aust, Yan Fan, Christian von Scheve, Malek Bajbouj
AGING & MENTAL HEALTH
(2018)