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Eidel M, Pfeiffer M, Ziebell P, Kübler A. Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface. Front Hum Neurosci 2024; 18:1371631. [PMID: 38957693 PMCID: PMC11218745 DOI: 10.3389/fnhum.2024.1371631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.
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Affiliation(s)
- M. Eidel
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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Chowdhury RR, Muhammad Y, Adeel U. Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN. SENSORS (BASEL, SWITZERLAND) 2023; 23:7908. [PMID: 37765965 PMCID: PMC10536894 DOI: 10.3390/s23187908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/23/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.
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Affiliation(s)
- Radia Rayan Chowdhury
- Department of Computing & Games, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
| | - Yar Muhammad
- Department of Computing & Games, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
- Department of Computer Science, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Usman Adeel
- Department of Computing & Games, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
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Siribunyaphat N, Punsawad Y. Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:2069. [PMID: 36850667 PMCID: PMC9964090 DOI: 10.3390/s23042069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.
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Affiliation(s)
| | - Yunyong Punsawad
- School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Informatics Innovative Center of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand
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Kim J, Jiang X, Forenzo D, Liu Y, Anderson N, Greco CM, He B. Immediate effects of short-term meditation on sensorimotor rhythm-based brain-computer interface performance. Front Hum Neurosci 2022; 16:1019279. [PMID: 36606248 PMCID: PMC9807599 DOI: 10.3389/fnhum.2022.1019279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Meditation has been shown to enhance a user's ability to control a sensorimotor rhythm (SMR)-based brain-computer interface (BCI). For example, prior work have demonstrated that long-term meditation practices and an 8-week mindfulness-based stress reduction (MBSR) training have positive behavioral and neurophysiological effects on SMR-based BCI. However, the effects of short-term meditation practice on SMR-based BCI control are still unknown. Methods In this study, we investigated the immediate effects of a short, 20-minute meditation on SMR-based BCI control. Thirty-seven subjects performed several runs of one-dimensional cursor control tasks before and after two types of 20-minute interventions: a guided mindfulness meditation exercise and a recording of a narrator reading a journal article. Results We found that there is no significant change in BCI performance and Electroencephalography (EEG) BCI control signal following either 20-minute intervention. Moreover, the change in BCI performance between the meditation group and the control group was found to be not significant. Discussion The present results suggest that a longer period of meditation is needed to improve SMR-based BCI control.
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Affiliation(s)
- Jeehyun Kim
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Xiyuan Jiang
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Dylan Forenzo
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Yixuan Liu
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Nancy Anderson
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Carol M. Greco
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
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Akram F, Alwakeel A, Alwakeel M, Hijji M, Masud U. A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials. SENSORS (BASEL, SWITZERLAND) 2022; 22:10000. [PMID: 36560369 PMCID: PMC9781197 DOI: 10.3390/s222410000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/06/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results.
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Affiliation(s)
- Faraz Akram
- Department of Biomedical Engineering, Riphah International University, Islamabad 46000, Pakistan
| | - Ahmed Alwakeel
- Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Mohammed Alwakeel
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Mohammad Hijji
- Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Usman Masud
- Faculty of Electrical and Electronics Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
- Department of Electrical Communication Engineering, University of Kassel, 34127 Kassel, Germany
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Zhao Z, Cui D, Wu G, Ren H, Zhu X, Xie W, Zhang Y, Yang L, Peng W, Lai C, Huang Y, Li H. Disrupted gut microbiota aggravates working memory dysfunction induced by high-altitude exposure in mice. Front Microbiol 2022; 13:1054504. [PMID: 36439863 PMCID: PMC9684180 DOI: 10.3389/fmicb.2022.1054504] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/24/2022] [Indexed: 01/12/2024] Open
Abstract
Background The widely accepted microbiome-gut-brain axis (MGBA) hypothesis may be essential for explaining the impact of high-altitude exposure on the human body, especially brain function. However, studies on this topic are limited, and the underlying mechanism remains unclear. Therefore, this study aimed to determine whether high-altitude-induced working memory dysfunction could be exacerbated with gut microbiota disruption. Methods and results C57BL/6 mice were randomly divided into three groups: control, high-altitude exposed (HAE), and high-altitude exposed with antibiotic treatment (HAE-A). The HAE and HAE-A groups were exposed to a low-pressure oxygen chamber (60-65 kPa) simulating the altitude of 3,500-4,000 m for 14 days, The air pressure level for the control group was maintained at 94.5 kPa. Antibiotic water (mixed with 0.2 g/L of ciprofloxacin and 1 g/L of metronidazole) was provided to the HAE-A group. Based on the results of the novel object test and P300 in the oddball behavioral paradigm training test, working memory dysfunction was aggravated by antibiotic treatment. We determined the antioxidant capacity in the prefrontal cortex and found a significant negative influence (p < 0.05) of disturbed gut microbiota on the total antioxidant capacity (T-AOC) and malondialdehyde (MDA) content, as well as the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px). The same trend was also observed in the apoptosis-related functional protein content and mRNA expression levels in the prefrontal cortex, especially the levels of bcl-2, Bax, and caspase-3. The high-altitude environment and antibiotic treatment substantially affected the richness and diversity of the colonic microbiota and reorganized the composition and structure of the microbial community. S24-7, Lachnospiraceae, and Lactobacillaceae were the three microbial taxa with the most pronounced differences under the stimulation by external factors in this study. In addition, correlation analysis between colonic microbiota and cognitive function in mice demonstrated that Helicobacteraceae may be closely related to behavioral results. Conclusion Disrupted gut microbiota could aggravate working memory dysfunction induced by high-altitude exposure in mice, indicating the existence of a link between high-altitude exposure and MGBA.
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Affiliation(s)
- Zhifang Zhao
- Department of Gastroenterology, National Institution of Drug Clinical Trial, Guizhou Provincial People's Hospital, Medical College of Guizhou University, Guiyang, Guizhou, China
| | - Dejun Cui
- Department of Gastroenterology, National Institution of Drug Clinical Trial, Guizhou Provincial People's Hospital, Medical College of Guizhou University, Guiyang, Guizhou, China
| | - Guosong Wu
- Department of Pharmacy, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hong Ren
- Plateau Brain Science Research Center, Tibet University, Lhasa, China
| | - Ximei Zhu
- Department of Pharmacy, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenting Xie
- Plateau Brain Science Research Center, Tibet University, Lhasa, China
| | - Yuming Zhang
- Plateau Brain Science Research Center, Tibet University, Lhasa, China
| | - Liu Yang
- Plateau Brain Science Research Center, Tibet University, Lhasa, China
| | - Weiqi Peng
- Department of Gastroenterology, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chunxiao Lai
- Department of Gastroenterology, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yongmei Huang
- Department of Pharmacy, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hao Li
- Plateau Brain Science Research Center, Tibet University, Lhasa, China
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Eidel M, Kübler A. Identifying potential training factors in a vibrotactile P300-BCI. Sci Rep 2022; 12:14006. [PMID: 35978082 PMCID: PMC9385085 DOI: 10.1038/s41598-022-18088-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/04/2022] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interfaces (BCI) often rely on visual stimulation and feedback. Potential end-users with impaired vision, however, cannot use these BCIs efficiently and require a non-visual alternative. Both auditory and tactile paradigms have been developed but are often not sufficiently fast or accurate. Thus, it is particularly relevant to investigate if and how users can train and improve performance. We report data from 29 healthy participants who trained with a 4-choice tactile P300-BCI during five sessions. To identify potential training factors, we pre-post assessed the robustness of the BCI performance against increased workload in a dual task condition and determined the participants' somatosensory sensitivity thresholds with a forced-choice intensity discrimination task. Accuracy (M = 79.2% to 92.0%) and tactually evoked P300 amplitudes increased significantly, confirming successful training. Pre-post somatosensory sensitivity increased, and workload decreased significantly, but results of the dual task condition remained inconclusive. The present study confirmed the previously reported feasibility and trainability of our tactile BCI paradigm within a multi-session design. Importantly, we provide first evidence of improvement in the somatosensory system as a potential mediator for the observed training effects.
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Affiliation(s)
- M Eidel
- Institute of Psychology, University of Würzburg, Würzburg, Germany.
| | - A Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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8
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Liu C, Jin J, Daly I, Li S, Sun H, Huang Y, Wang X, Cichocki A. SincNet-based Hybrid Neural Network for Motor Imagery EEG Decoding. IEEE Trans Neural Syst Rehabil Eng 2022; 30:540-549. [PMID: 35235515 DOI: 10.1109/tnsre.2022.3156076] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets.
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9
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Han Y, Ziebell P, Riccio A, Halder S. Two sides of the same coin: adaptation of BCIs to internal states with user-centered design and electrophysiological features. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2041294] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Yiyuan Han
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Philipp Ziebell
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Angela Riccio
- Neuroelectrical Imaging and Brain Computer Interface Laboratory,Fondazione Santa Lucia, Irccs, Rome, Italy
| | - Sebastian Halder
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
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10
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Huggins JE, Krusienski D, Vansteensel MJ, Valeriani D, Thelen A, Stavisky S, Norton JJS, Nijholt A, Müller-Putz G, Kosmyna N, Korczowski L, Kapeller C, Herff C, Halder S, Guger C, Grosse-Wentrup M, Gaunt R, Dusang AN, Clisson P, Chavarriaga R, Anderson CW, Allison BZ, Aksenova T, Aarnoutse E. Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier. BRAIN-COMPUTER INTERFACES 2022; 9:69-101. [PMID: 36908334 PMCID: PMC9997957 DOI: 10.1080/2326263x.2021.2009654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022]
Abstract
The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.
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Affiliation(s)
- Jane E Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744, 734-936-7177
| | - Dean Krusienski
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23219
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Dept of Neurosurgery, University Medical Center Utrecht, The Netherlands
| | | | - Antonia Thelen
- eemagine Medical Imaging Solutions GmbH, Berlin, Germany
| | | | - James J S Norton
- National Center for Adaptive Neurotechnologies, US Department of Veterans Affairs, 113 Holland Ave, Albany, NY 12208
| | - Anton Nijholt
- Faculty EEMCS, University of Twente, Enschede, The Netherlands
| | - Gernot Müller-Putz
- Institute of Neural Engineering, GrazBCI Lab, Graz University of Technology, Stremayrgasse 16/4, 8010 Graz, Austria
| | - Nataliya Kosmyna
- Massachusetts Institute of Technology (MIT), Media Lab, E14-548, Cambridge, MA 02139, Unites States
| | | | | | - Christian Herff
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Christoph Guger
- g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria, +43725122240-0
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, Vienna Cognitive Science Hub, Data Science @ Uni Vienna University of Vienna
| | - Robert Gaunt
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA, 3520 5th Ave, Suite 300, Pittsburgh, PA 15213, 412-383-1426
| | - Aliceson Nicole Dusang
- Department of Electrical and Computer Engineering, School of Engineering, Brown University, Carney Institute for Brain Science, Brown University, Providence, RI
- Department of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA
| | | | - Ricardo Chavarriaga
- IEEE Standards Association Industry Connections group on neurotechnologies for brain-machine interface, Center for Artificial Intelligence, School of Engineering, ZHAW-Zurich University of Applied Sciences, Switzerland, Switzerland
| | - Charles W Anderson
- Department of Computer Science, Molecular, Cellular and Integrative Neurosience Program, Colorado State University, Fort Collins, CO 80523
| | - Brendan Z Allison
- Dept. of Cognitive Science, Mail Code 0515, University of California at San Diego, La Jolla, United States, 619-534-9754
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Erik Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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11
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Gonzalez-Navarro P, Celik B, Moghadamfalahi M, Akcakaya M, Fried-Oken M, Erdoğmuş D. Feedback Related Potentials for EEG-Based Typing Systems. Front Hum Neurosci 2022; 15:788258. [PMID: 35145386 PMCID: PMC8821166 DOI: 10.3389/fnhum.2021.788258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.
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Affiliation(s)
- Paula Gonzalez-Navarro
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- *Correspondence: Paula Gonzalez-Navarro
| | - Basak Celik
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Basak Celik
| | | | - Murat Akcakaya
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PI, United States
| | - Melanie Fried-Oken
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR, United States
| | - Deniz Erdoğmuş
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Deniz Erdoğmuş
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12
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Kubacki A. Use of Force Feedback Device in a Hybrid Brain-Computer Interface Based on SSVEP, EOG and Eye Tracking for Sorting Items. SENSORS 2021; 21:s21217244. [PMID: 34770554 PMCID: PMC8588340 DOI: 10.3390/s21217244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022]
Abstract
Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EEG signal recording devices and ever-lower set prices. Unfortunately, such systems are substantially limited in terms of the number of generated commands. This especially applies to sets that are not medical devices. This article proposes a hybrid brain-computer system based on the Steady-State Visual Evoked Potential (SSVEP), EOG, eye tracking, and force feedback system. Such an expanded system eliminates many of the particular system shortcomings and provides much better results. The first part of the paper presents information on the methods applied in the hybrid brain-computer system. The presented system was tested in terms of the ability of the operator to place the robot’s tip to a designated position. A virtual model of an industrial robot was proposed, which was used in the testing. The tests were repeated on a real-life industrial robot. Positioning accuracy of system was verified with the feedback system both enabled and disabled. The results of tests conducted both on the model and on the real object clearly demonstrate that force feedback improves the positioning accuracy of the robot’s tip when controlled by the operator. In addition, the results for the model and the real-life industrial model are very similar. In the next stage, research was carried out on the possibility of sorting items using the BCI system. The research was carried out on a model and a real robot. The results show that it is possible to sort using bio signals from the human body.
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Affiliation(s)
- Arkadiusz Kubacki
- Institute of Mechanical Technology, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznań, Poland
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Chu C, Luo J, Tian X, Han X, Guo S. A P300 Brain-Computer Interface Paradigm Based on Electric and Vibration Simple Command Tactile Stimulation. Front Hum Neurosci 2021; 15:641357. [PMID: 33935672 PMCID: PMC8081187 DOI: 10.3389/fnhum.2021.641357] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/09/2021] [Indexed: 12/15/2022] Open
Abstract
This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.
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Affiliation(s)
- Chenxi Chu
- Institute of Artificial Intelligence (AI) and Robotics, Academy for Engineering and Technology, Fudan University, as well as Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai, China
- Guanghua Lingang Engineering Application and Technology R&D (Shanghai) Co., Ltd., Shanghai, China
| | - Jingjing Luo
- Institute of Artificial Intelligence (AI) and Robotics, Academy for Engineering and Technology, Fudan University, as well as Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai, China
- Jihua Laboratory, Guangzhou, China
| | - Xiwei Tian
- Department of the State Key Laboratory of Reliability and Intelligence of Electrical Equipment and The Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China
| | - Xiangke Han
- Department of the State Key Laboratory of Reliability and Intelligence of Electrical Equipment and The Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China
| | - Shijie Guo
- Institute of Artificial Intelligence (AI) and Robotics, Academy for Engineering and Technology, Fudan University, as well as Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai, China
- Guanghua Lingang Engineering Application and Technology R&D (Shanghai) Co., Ltd., Shanghai, China
- Department of the State Key Laboratory of Reliability and Intelligence of Electrical Equipment and The Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China
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Kawala-Sterniuk A, Browarska N, Al-Bakri A, Pelc M, Zygarlicki J, Sidikova M, Martinek R, Gorzelanczyk EJ. Summary of over Fifty Years with Brain-Computer Interfaces-A Review. Brain Sci 2021; 11:43. [PMID: 33401571 PMCID: PMC7824107 DOI: 10.3390/brainsci11010043] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/25/2020] [Accepted: 12/27/2020] [Indexed: 11/16/2022] Open
Abstract
Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.
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Affiliation(s)
- Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Natalia Browarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Amir Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, 51001 Babylon, Iraq;
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
- Department of Computing and Information Systems, University of Greenwich, London SE10 9LS, UK
| | - Jaroslaw Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.S.); (R.M.)
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.S.); (R.M.)
| | - Edward Jacek Gorzelanczyk
- Department of Theoretical Basis of BioMedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067 Bydgoszcz, Poland;
- Institute of Philosophy, Kazimierz Wielki University, 85-092 Bydgoszcz, Poland
- Babinski Specialist Psychiatric Healthcare Center, Outpatient Addiction Treatment, 91-229 Lodz, Poland
- The Society for the Substitution Treatment of Addiction “Medically Assisted Recovery”, 85-791 Bydgoszcz, Poland
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Mowla MR, Gonzalez-Morales JD, Rico-Martinez J, Ulichnie DA, Thompson DE. A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation. Brain Sci 2020; 10:brainsci10100734. [PMID: 33066374 PMCID: PMC7602195 DOI: 10.3390/brainsci10100734] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/02/2020] [Accepted: 10/06/2020] [Indexed: 11/16/2022] Open
Abstract
P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.
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Affiliation(s)
- Md Rakibul Mowla
- Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (J.D.G.-M.); (J.R.-M.)
- Correspondence: (M.R.M.); (D.E.T.)
| | - Jesus D. Gonzalez-Morales
- Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (J.D.G.-M.); (J.R.-M.)
| | - Jacob Rico-Martinez
- Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (J.D.G.-M.); (J.R.-M.)
| | - Daniel A. Ulichnie
- Department of Biomedical Engineering, Wichita State University, Wichita, KS 67260, USA;
| | - David E. Thompson
- Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (J.D.G.-M.); (J.R.-M.)
- Correspondence: (M.R.M.); (D.E.T.)
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