Article
Chemistry, Analytical
Xavier Duart, Eduardo Quiles, Ferran Suay, Nayibe Chio, Emilio Garcia, Francisco Morant
Summary: The study compared white, red, and green flashing stimuli at three frequencies (5, 12, and 30 Hz) to find that the middle frequency generated the best SNR. White showed as good an SNR as red at 12 Hz, and green at 5 Hz. There was a correlation between attention and SNR at low frequency.
Article
Computer Science, Artificial Intelligence
Francisco Laport, Adriana Dapena, Paula M. Castro, Daniel I. Iglesias, Francisco J. Vazquez-Araujo
Summary: Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. This paper presents and compares the accuracy and robustness of an EEG system for detecting eye movements, demonstrating that employing a system with two channels and using SVM, DT, or LR classifiers enhances the performance compared to a single-channel setup.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Chemistry, Analytical
Mateo Tobon-Henao, Andres Alvarez-Meza, German Castellanos-Dominguez
Summary: The EEG-based motor imagery paradigm is widely studied in the field of Brain-Computer Interface (BCI) development, but it faces challenges due to the low Signal-to-Noise Ratio (SNR). This paper proposes a subject-dependent preprocessing approach that uses Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts and improve the classification performance in subjects with poor motor skills.
Article
Engineering, Biomedical
Faghihe Massaeli, Mohammad Bagheri, Sarah D. Power
Summary: A passive brain-computer interface (pBCI) enhances human-machine interaction by monitoring the user's mental state and making appropriate modifications based on this information. The ability to detect the specific type of attentional resources required, such as visual or auditory, is important for pBCI development. This study investigated the ability of electroencephalography (EEG) to distinguish between auditory and visual processing tasks at different levels of demand.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Saleh Alzahrani, Charles W. Anderson
Summary: The study indicates that using tri-polar concentric ring electrodes (TCREs) to record movement-related potentials (MRPs) results in higher spatial resolution and accuracy compared to disc electrodes, reducing information overlap between neighboring locations. In experiments involving real and imaginary finger movements, TCREs outperformed disc electrodes in terms of recording quality.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Neurosciences
Soroush Mirjalili, Patrick Powell, Jonathan Strunk, Taylor James, Audrey Duarte
Summary: Previous studies have explored classifying single-trial neural responses using machine learning to provide real-time interventions for memory and cognitive states, finding that extracting multiple feature types is more effective than single type, and combination methods for feature selection are crucial for selecting effective features in classification processes.
Article
Clinical Neurology
Zeanna Jadavji, Jack Zhang, Brett Paffrath, Ephrem Zewdie, Adam Kirton
Summary: This study investigated the ability of children with perinatal stroke to operate a simple BCI system. The results showed that they can achieve proficiency in basic tasks using BCI systems, with no significant difference compared to typically developing controls.
Article
Computer Science, Information Systems
Saleh I. Alzahrani, Mashael M. Alsaleh
Summary: Smoothing filters are used in EEG data analysis to remove noise and preserve signal morphology. This study compared the effects of three smoothing filters on EEG data and found that regularization smoothing improved the correlation between target and nontarget responses, increased signal-to-noise ratio, and provided more discriminative information about motor imagery movements.
Article
Neurosciences
Guangye Li, Shize Jiang, Jianjun Meng, Guohong Chai, Zehan Wu, Zhen Fan, Jie Hu, Xinjun Sheng, Dingguo Zhang, Liang Chen, Xiangyang Zhu
Summary: The study revealed that multiple brain regions show significant neural selectivity to the task, with sensorimotor areas carrying rich discriminative information for decoding and other regions gradually contributing less but still providing useful information for extracting movement parameters. Among different spectral components of SEEG activity, high gamma and delta bands offer the most informative features for hand movements reconstruction.
Article
Engineering, Electrical & Electronic
Yuang Zhang, Xiangwei Zheng, Weizhi Xu, Hong Liu
Summary: Eye blinks play an important role in EEG signals, as they can heavily impact the signals and provide useful information for BCI and scientific applications. This study proposes a real-time blink detection method called RT-Blink, which utilizes a short window and a random forest model to automatically identify the duration of blinks from a single-channel EEG signal. The results show promising potential for real-time EEG applications.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Aiping Liu, Qingze Liu, Xu Zhang, Xiang Chen, Xun Chen
Summary: This study investigates two major muscle artifact removal schemes in a dynamic scenario, showing that using auxiliary EMG channels for RLS filtering can significantly improve classification accuracy of SSVEP data in a few-channel condition.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Multidisciplinary
Pasquale Arpaia, Damien Coyle, Francesco Donnarumma, Antonio Esposito, Angela Natalizio, Marco Parvis
Summary: This paper presents a wearable brain-computer interface that enhances motor imagery training through neurofeedback in extended reality. Various feedback modalities, including visual and vibrotactile, were evaluated either singularly or simultaneously. The results showed statistically significant improvement in performance over multiple sessions, demonstrating the functionality of the motor imagery-based instrument even with minimal equipment. The best feedback modality was found to be subject-dependent, with classification accuracy exceeding 80% in some cases.
Article
Neurosciences
Zhengqing Miao, Meirong Zhaoa, Xin Zhangbc, Dong Ming
Summary: LMDA-Net is a lightweight multi-dimensional attention network that effectively integrates features from EEG signals, resulting in improved classification performance. Experimental results demonstrate its superiority in various BCI tasks and its high interpretability.
Article
Chemistry, Analytical
Mahsa Bagheri, Sarah D. Power
Summary: Research on EEG-based mental workload detection for a passive BCI primarily focuses on classifying cognitive states associated with task performance, without considering other aspects of the user's mental state. However, in real-life situations, both cognitive and affective states often change simultaneously, and relying on just one state in a BCI system may lead to unreliable results. Furthermore, simultaneous prediction of multiple mental states may be critical for maximizing the practical effectiveness of real-life online BCI systems. This study explores the feasibility of simultaneous classification of mental workload and stress level in an online passive BCI, achieving promising accuracies using cross-subject classification with transfer learning.
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)
Article
Behavioral Sciences
Shabah M. Shadli, Olivia High, Bede Byers, Polly Gibbs, Rubina Steller, Paul Glue, Neil McNaughton
BEHAVIORAL NEUROSCIENCE
(2020)
Article
Behavioral Sciences
Shabah M. Shadli, Julia McIntosh, Neil McNaughton
BEHAVIORAL NEUROSCIENCE
(2020)
Article
Clinical Neurology
Paul Glue, Shona Neehoff, Amandine Sabadel, Lucy Broughton, Martin Le Nedelec, Shabah Shadli, Neil McNaughton, Natalie J. Medlicott
JOURNAL OF PSYCHOPHARMACOLOGY
(2020)
Article
Multidisciplinary Sciences
Tame N. J. Kawe, Shabah M. Shadli, Neil McNaughton
SCIENTIFIC REPORTS
(2019)
Article
Neurosciences
Phoebe S. -H. Neo, Jessica Tinker, Neil McNaughton
FRONTIERS IN NEUROSCIENCE
(2020)
Article
Behavioral Sciences
Imogen Kaack, Jungwoo Chae, Shabah Mohammad Shadli, Kristin Hillman
COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE
(2020)
Article
Behavioral Sciences
Shabah M. Shadli, Vidusha Tewari, Jack Holden, Neil McNaughton
Summary: Anxiety disorder biomarker GCSR shows differences between left-handers and right-handers, with left-handers mainly mirroring the responses of right-handers. The study suggests that when using GCSR as a biomarker in groups with both left and right-handed individuals, data from the channel ipsilateral to the dominant hand should be utilized.
Article
Multidisciplinary Sciences
Shabah M. Shadli, Lynne C. Ando, Julia McIntosh, Veema Lodhia, Bruce R. Russell, Ian J. Kirk, Paul Glue, Neil McNaughton
Summary: The study suggests that the theoretical biomarker GCSR can help diagnose and redefine psychiatric disorders. Research shows that anxiolytic drugs reduce GCSR and correlate with anxiety scores.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Shabah M. Shadli, Robert G. Delany, Paul Glue, Neil McNaughton
Summary: Anxiety disorders are the most prevalent mental disorders in the world, causing significant economic burdens and reducing quality of life. Ketamine has been found to be an effective anxiolytic, even in cases resistant to conventional treatments, but its therapeutic mechanism is still unknown.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Behavioral Sciences
Christopher F. Sharpley, Vicki Bitsika, Shabah M. Shadli, Emmanuel Jesulola, Linda L. Agnew
Summary: This study investigated the relationship between frontal lobe asymmetry (FLA) and depression, finding meaningful associations between FLA and four subtypes of depression. The study also revealed different patterns of association between FLA and depression subtypes according to sex and depression severity.
BEHAVIOURAL BRAIN RESEARCH
(2023)
Article
Psychiatry
Christopher F. Sharpley, Vicki Bitsika, Wayne M. Arnold, Shabah M. Shadli, Emmanuel Jesulola, Linda L. Agnew
Summary: Although depression is widespread and carries a major disease burden, current treatments remain non-universally effective, possibly due to the heterogeneity of depression. This study investigated the associations between a model of depressive behavior subtypes and frontal lobe asymmetry using a different data analytic procedure. Network analysis revealed unique neurophysiological profiles for each of the four depressive behavior subtypes.
FRONTIERS IN PSYCHIATRY
(2023)
Article
Medical Informatics
Phoebe S. -H. Neo, Terence Mayne, Xiping Fu, Zhiyi Huang, Elizabeth A. Franz
Summary: Crosstalk during motor imagery can negatively impact BCI performance, as shown in this study. Different types of imagined movements have varying classification accuracies, underscoring the importance of task choice in BCI design.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Shenghuan Zhang, Brendan McCane, Phoebe S-H Neo, Shabah M. Shadli, Neil McNaughton
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Yi Wang, Brendan McCane, Neil McNaughton, Zhiyi Huang, Shabah Shadli, Phoebe Neo
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2019)
Article
Biochemical Research Methods
Aline Silva da Cruz, Maria Margarida Drehmer, Wagner Baetas-da-Cruz, Joao Carlos Machado
Summary: This study quantified microcirculation cerebral blood flow in a rat model of ischemic stroke using ultrasound biomicroscopy and ultrasound contrast agents. The results showed high sensitivity and specificity of this method, making it a valuable tool for preclinical studies.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Christina Dalla, Ivana Jaric, Pavlina Pavlidi, Georgia E. Hodes, Nikolaos Kokras, Anton Bespalov, Martien J. Kas, Thomas Steckler, Mohamed Kabbaj, Hanno Wuerbel, Jordan Marrocco, Jessica Tollkuhn, Rebecca Shansky, Debra Bangasser, Jill B. Becker, Margaret McCarthy, Chantelle Ferland-Beckham
Summary: Many funding agencies have emphasized the importance of considering sex as a biological variable in experimental design to improve the reproducibility and translational relevance of preclinical research. Omitting the female sex from experimental designs in neuroscience and pharmacology can result in biased or limited understanding of disease mechanisms. This article provides methodological considerations for incorporating sex as a biological variable in in vitro and in vivo experiments, including the influence of age and hormone levels, and proposes strategies to enhance methodological rigor and translational relevance in preclinical research.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Wenyu Gu, Dongxu Li, Jia-Hong Gao
Summary: We developed a precise and rapid method for positioning and labelling triaxial OPMs on a wearable magnetoencephalography (MEG) system, improving the efficiency of OPM positioning and labelling.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Kai Lin, Linhang Zhang, Jing Cai, Jiaqi Sun, Wenjie Cui, Guangda Liu
Summary: The article introduces an EEG feature map processing model for emotion recognition, which achieves significantly improved accuracy by fusing EEG information at different spatial scales and introducing a channel attention mechanism.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
John E. Parker, Asier Aristieta, Aryn H. Gittis, Jonathan E. Rubin
Summary: This work presents a toolbox that implements a methodology for automated classification of neural responses based on spike train recordings. The toolbox provides a user-friendly and efficient approach to detect various types of neuronal responses that may not be identified by traditional methods.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Summary: This study compared the performance of SVM and CNN on the same datasets and found that CNN achieved consistently higher classification accuracies. The classification accuracies of SVM and CNN were generally not correlated, and the heatmaps derived from them did not overlap significantly.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Antonino Visalli, Maria Montefinese, Giada Viviani, Livio Finos, Antonino Vallesi, Ettore Ambrosini
Summary: This study introduces an analytical strategy that allows the use of mixed-effects models (LMM) in mass univariate analyses of EEG data. The proposed method overcomes the computational costs and shows excellent performance properties, making it increasingly important in the field of neuroscience.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Xavier Cano-Ferrer, Alexandra Tran -Van -Minh, Ede Rancz
Summary: This study developed a novel rotation platform for studying neural processes and spatial navigation. The platform is modular, affordable, and easy to build, and can be driven by the experimenter or animal movement. The research demonstrated the utility of the platform, which combines the benefits of head fixation and intact vestibular activity.
JOURNAL OF NEUROSCIENCE METHODS
(2024)