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Xu W, Tang J, Qi H. Using the Cocktail Party Effect to Add the Coding Dimension of Auditory Event Related Potential Brain-Computer Interface. IEEE J Biomed Health Inform 2024; 28:5953-5961. [PMID: 38896526 DOI: 10.1109/jbhi.2024.3416488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
OBJECTIVE The auditory event-related potential based brain-computer interface (aERP-BCI) is a classical paradigm of brain-computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect. METHODS The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed. CONCLUSION The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC. SIGNIFICANCE The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.
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Choi YJ, Kwon OS, Kim SP. Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support. Cogn Neurodyn 2023; 17:1401-1416. [PMID: 37974580 PMCID: PMC10640544 DOI: 10.1007/s11571-022-09901-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/05/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022] Open
Abstract
Non-invasive brain-computer interfaces (BCIs) based on an event-related potential (ERP) component, P300, elicited via the oddball paradigm, have been extensively developed to enable device control and communication. While most P300-based BCIs employ visual stimuli in the oddball paradigm, auditory P300-based BCIs also need to be developed for users with unreliable gaze control or limited visual processing. Specifically, auditory BCIs without additional visual support or multi-channel sound sources can broaden the application areas of BCIs. This study aimed to design optimal stimuli for auditory BCIs among artificial (e.g., beep) and natural (e.g., human voice and animal sounds) sounds in such circumstances. In addition, it aimed to investigate differences between auditory and visual stimulations for online P300-based BCIs. As a result, natural sounds led to both higher online BCI performance and larger differences in ERP amplitudes between the target and non-target compared to artificial sounds. However, no single type of sound offered the best performance for all subjects; rather, each subject indicated different preferences between the human voice and animal sound. In line with previous reports, visual stimuli yielded higher BCI performance (average 77.56%) than auditory counterparts (average 54.67%). In addition, spatiotemporal patterns of the differences in ERP amplitudes between target and non-target were more dynamic with visual stimuli than with auditory stimuli. The results suggest that selecting a natural auditory stimulus optimal for individual users as well as making differences in ERP amplitudes between target and non-target stimuli more dynamic may further improve auditory P300-based BCIs. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09901-3.
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Affiliation(s)
- Yun-Joo Choi
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919 Korea
| | - Oh-Sang Kwon
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919 Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919 Korea
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Kurmanavičiūtė D, Kataja H, Jas M, Välilä A, Parkkonen L. Target of selective auditory attention can be robustly followed with MEG. Sci Rep 2023; 13:10959. [PMID: 37414861 PMCID: PMC10325959 DOI: 10.1038/s41598-023-37959-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023] Open
Abstract
Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of [Formula: see text] (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface.
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Affiliation(s)
- Dovilė Kurmanavičiūtė
- Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, 00076, Aalto, Finland.
| | - Hanna Kataja
- Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, 00076, Aalto, Finland
| | - Mainak Jas
- Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, 00076, Aalto, Finland
- Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Charlestown, MA, 02129, USA
| | - Anne Välilä
- Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, 00076, Aalto, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, 00076, Aalto, Finland
- Aalto NeuroImaging, Aalto University, 00076, Aalto, Finland
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Sosulski J, Tangermann M. Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces. J Neural Eng 2022; 19. [PMID: 36270502 DOI: 10.1088/1741-2552/ac9c98] [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: 05/19/2022] [Accepted: 10/21/2022] [Indexed: 01/07/2023]
Abstract
Objective.Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain-computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates.Approach.We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel.Main results.An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this 'ToeplitzLDA' compared to shrinkage regularized LDA (up to 6 AUC points,p < 0.001) and Riemannian classification approaches (up to 2 AUC points,p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features.Significance.The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.
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Affiliation(s)
- Jan Sosulski
- Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Michael Tangermann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Visuo-auditory stimuli with semantic, temporal and spatial congruence for a P300-based BCI: An exploratory test with an ALS patient in a completely locked-in state. J Neurosci Methods 2022; 379:109661. [PMID: 35817307 DOI: 10.1016/j.jneumeth.2022.109661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/25/2022] [Accepted: 07/04/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Brain-computer interfaces (BCIs) are a promising tool for communication with completely locked-in state (CLIS) patients. Despite the great efforts already made by the BCI research community, the cases of success are still very few, very exploratory, limited in time, and based on simple 'yes/no' paradigms. NEW METHOD A P300-based BCI is proposed comparing two conditions, one corresponding to purely spatial auditory stimuli (AU-S) and the other corresponding to hybrid visual and spatial auditory stimuli (HVA-S). In the HVA-S condition, there is a semantic, temporal, and spatial congruence between visual and auditory stimuli. The stimuli comprise a lexicon of 7 written and spoken words. Spatial sounds are generated through the head-related transfer function. Given the good results obtained with 10 able-bodied participants, we investigated whether a patient entering CLIS could use the proposed BCI. RESULTS The able-bodied group achieved 71.3 % and 90.5 % online classification accuracy for the auditory and hybrid BCIs respectively, while the patient achieved 30 % and chance level accuracies, for the same conditions. Notwithstanding, the patient's event-related potentials (ERPs) showed statistical discrimination between target and non-target events in different time windows. COMPARISON WITH EXISTING METHODS The results of the control group compare favorably with the state-of-the-art, considering a 7-class BCI controlled visual-covertly and with auditory stimuli. The integration of visual and auditory stimuli has not been tested before with CLIS patients. CONCLUSIONS The semantic, temporal, and spatial congruence of the stimuli increased the performance of the control group, but not of the CLIS patient, which can be due to impaired attention and cognitive function. The patient's unique ERP patterns make interpretation difficult, requiring further tests/paradigms to decouple patients' responses at different levels (reflexive, perceptual, cognitive). The ERPs discrimination found indicates that a simplification of the proposed approaches may be feasible.
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Musso M, Hübner D, Schwarzkopf S, Bernodusson M, LeVan P, Weiller C, Tangermann M. OUP accepted manuscript. Brain Commun 2022; 4:fcac008. [PMID: 35178518 PMCID: PMC8846581 DOI: 10.1093/braincomms/fcac008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/22/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Mariacristina Musso
- Department of Neurology and Neurophysiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
| | - David Hübner
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
- Brain State Decoding Lab, Department of Computer Science, Technical Faculty, University of Freiburg, Germany
| | - Sarah Schwarzkopf
- Department of Neurology and Neurophysiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
| | - Maria Bernodusson
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
- Department of Radiology—Medical Physics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
- Department of Radiology—Medical Physics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Department of Radiology, Cumming School of Medicine, University of Calgary, Canada
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Canada
| | - Cornelius Weiller
- Department of Neurology and Neurophysiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
| | - Michael Tangermann
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Germany
- Brain State Decoding Lab, Department of Computer Science, Technical Faculty, University of Freiburg, Germany
- Department of Artificial Intelligence, Donders Institute, Radboud University, Nijmegen, The Netherlands
- Correspondence to: Michael Tangermann Donders Institute, Radboud University Thomas van Aquinostraat 4 6525 GD Nijmegen, The Netherlands E-mail:
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Velasco-Álvarez F, Fernández-Rodríguez Á, Medina-Juliá MT, Ron-Angevin R. Speech stream segregation to control an ERP-based auditory BCI. J Neural Eng 2021; 18. [PMID: 33470970 DOI: 10.1088/1741-2552/abdd44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/19/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The use of natural sounds in auditory Brain-Computer Interfaces (BCI) has been shown to improve classification results and usability. Some auditory BCIs are based on stream segregation, in which the subjects must attend one audio stream and ignore the other(s); these streams include some kind of stimuli to be detected. In this work we focus on Event-Related Potentials (ERP) and study whether providing intelligible content to each audio stream could help the users to better concentrate on the desired stream and so to better attend the target stimuli and to ignore the non-target ones. APPROACH In addition to a control condition, two experimental conditions, based on the selective attention and the cocktail party effect, were tested using two simultaneous and spatialized audio streams: i) the condition A2 consisted of an overlap of auditory stimuli (single syllables) on a background consisting of natural speech for each stream, ii) in condition A3, brief alterations of the natural flow of each speech were used as stimuli. MAIN RESULTS The two experimental proposals improved the results of the control condition (single words as stimuli without a speech background) both in a cross validation analysis of the calibration part and in the online test. The analysis of the ERP responses also presented better discriminability for the two proposals in comparison to the control condition. The results of subjective questionnaires support the better usability of the first experimental condition. SIGNIFICANCE The use of natural speech as background improves the stream segregation in an ERP-based auditory BCI (with significant results in the performance metrics, the ERP waveforms, and in the preference parameter in subjective questionnaires). Future work in the field of ERP-based stream segregation should study the use of natural speech in combination with easily perceived but not distracting stimuli.
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Affiliation(s)
- Francisco Velasco-Álvarez
- Department of Electronic Technology, Universidad de Malaga, E.T.S.I. Telecomunicación, Campus de Teatinos s/n, Malaga, 29071, SPAIN
| | - Álvaro Fernández-Rodríguez
- Department of Electronic Technology, University of Málaga, E.T.S.I. Telecomunicación, Campus de Teatinos s/n, Málaga, 29071, SPAIN
| | - M Teresa Medina-Juliá
- Department of Electronic Technology, Universidad de Malaga, E.T.S.I. Telecomunicación, Campus de Teatinos s/n, Malaga, 29071, SPAIN
| | - Ricardo Ron-Angevin
- Department of Electronic Technology, Universidad de Malaga, E.T.S.I. Telecomunicación, Campus de Teatinos s/n, Malaga, 29071, SPAIN
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Abstract
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.
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Sosulski J, Tangermann M. Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2256-2260. [PMID: 31946349 DOI: 10.1109/embc.2019.8857460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.
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Baek HJ, Chang MH, Heo J, Park KS. Enhancing the Usability of Brain-Computer Interface Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:5427154. [PMID: 31316556 PMCID: PMC6604478 DOI: 10.1155/2019/5427154] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 05/02/2019] [Accepted: 05/14/2019] [Indexed: 11/17/2022]
Abstract
Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.
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Affiliation(s)
- Hyun Jae Baek
- Department of Medical and Mechatronics Engineering, Soonchunhyang University, Asan, Republic of Korea
| | - Min Hye Chang
- Korea Electrotechnology Research Institute (KERI), Ansan, Republic of Korea
| | - Jeong Heo
- Artificial Intelligence Laboratory, Software Center, LG Electronics, Seoul, Republic of Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea
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Onishi A, Nakagawa S. How Does the Degree of Valence Influence Affective Auditory P300-Based BCIs? Front Neurosci 2019; 13:45. [PMID: 30837822 PMCID: PMC6390079 DOI: 10.3389/fnins.2019.00045] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 01/17/2019] [Indexed: 11/29/2022] Open
Abstract
A brain-computer interface (BCI) translates brain signals into commands for the control of devices and for communication. BCIs enable persons with disabilities to communicate externally. Positive and negative affective sounds have been introduced to P300-based BCIs; however, how the degree of valence (e.g., very positive or positive) influences the BCI has not been investigated. To further examine the influence of affective sounds in P300-based BCIs, we applied sounds with five degrees of valence to the P300-based BCI. The sound valence ranged from very negative to very positive, as determined by Scheffe's method. The effect of sound valence on the BCI was evaluated by waveform analyses, followed by the evaluation of offline stimulus-wise classification accuracy. As a result, the late component of P300 showed significantly higher point-biserial correlation coefficients in response to very positive and very negative sounds than in response to the other sounds. The offline stimulus-wise classification accuracy was estimated from a region-of-interest. The analysis showed that the very negative sound achieved the highest accuracy and the very positive sound achieved the second highest accuracy, suggesting that the very positive sound and the very negative sound may be required to improve the accuracy.
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Affiliation(s)
- Akinari Onishi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Seiji Nakagawa
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.,Department of Medical Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.,University Hospital Med-Tech Link Center, Chiba University, Chiba, Japan
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Hübner D, Schall A, Prange N, Tangermann M. Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials. Front Hum Neurosci 2018; 12:391. [PMID: 30323749 PMCID: PMC6172854 DOI: 10.3389/fnhum.2018.00391] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/10/2018] [Indexed: 11/13/2022] Open
Abstract
Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. In this work, we investigate the option to use a BCI with eyes-closed. Twelve healthy subjects participated in a single electroencephalography (EEG) session where they were listening to a rapid stream of bisyllabic words while alternatively having their eyes open or closed. In addition, we assessed usability aspects for the two conditions with a questionnaire. Our analysis shows that eyes-closed does not reduce the number of eye artifacts and that event-related potential (ERP) responses and classification accuracies are comparable between both conditions. Importantly, we found that subjects expressed a significant general preference toward the eyes-closed condition and were also less tensed in that condition. Furthermore, switching between eyes-closed and eyes-open and vice versa is possible without a severe drop in classification accuracy. These findings suggest that eyes-closed should be considered as a viable alternative in auditory BCIs that might be especially useful for subjects with limited control over their eye movements.
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Affiliation(s)
- David Hübner
- Brain State Decoding Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Cluster of Excellence, BrainLinks-BrainTools, Freiburg, Germany
| | - Albrecht Schall
- Brain State Decoding Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Natalie Prange
- Brain State Decoding Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Michael Tangermann
- Brain State Decoding Lab, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Cluster of Excellence, BrainLinks-BrainTools, Freiburg, Germany
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Kellmeyer P, Grosse-Wentrup M, Schulze-Bonhage A, Ziemann U, Ball T. Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis-implications for brain-computer interfacing. J Neural Eng 2018; 15:041003. [PMID: 29676287 DOI: 10.1088/1741-2552/aabfa5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE For patients with amyotrophic lateral sclerosis (ALS) who are suffering from severe communication or motor problems, brain-computer interfaces (BCIs) can improve the quality of life and patient autonomy. However, current BCI systems are not as widely used as their potential and patient demand would let assume. This underutilization is a result of technological as well as user-based limitations but also of the comparatively poor performance of currently existing BCIs in patients with late-stage ALS, particularly in the locked-in state. APPROACH Here we review a broad range of electrophysiological studies in ALS patients with the aim to identify electrophysiological correlates of ALS-related neurodegeneration in motor and non-motor brain regions in to better understand potential neurophysiological limitations of current BCI systems for ALS patients. To this end we analyze studies in ALS patients that investigated basic sensory evoked potentials, resting-state and task-based paradigms using electroencephalography or electrocorticography for basic research purposes as well as for brain-computer interfacing. Main results and significance. Our review underscores that, similarly to mounting evidence from neuroimaging and neuropathology, electrophysiological measures too indicate neurodegeneration in non-motor areas in ALS. Furthermore, we identify an unexpected gap of basic and advanced electrophysiological studies in late-stage ALS patients, particularly in the locked-in state. We propose a research strategy on how to fill this gap in order to improve the design and performance of future BCI systems for this patient group.
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Affiliation(s)
- Philipp Kellmeyer
- Translational Neurotechnology Lab, Department of Neurosurgery, Medical Center-University of Freiburg, Freiburg im Breisgau, Germany. Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany
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Abu-Alqumsan M, Kapeller C, Hintermüller C, Guger C, Peer A. Invariance and variability in interaction error-related potentials and their consequences for classification. J Neural Eng 2017; 14:066015. [DOI: 10.1088/1741-2552/aa8416] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Huang M, Jin J, Zhang Y, Hu D, Wang X. Usage of drip drops as stimuli in an auditory P300 BCI paradigm. Cogn Neurodyn 2017; 12:85-94. [PMID: 29435089 DOI: 10.1007/s11571-017-9456-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 07/17/2017] [Accepted: 10/10/2017] [Indexed: 11/28/2022] Open
Abstract
Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP (p < 0.05, Wilcoxon signed test; p < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty (p < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.
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Affiliation(s)
- Minqiang Huang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yu Zhang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Dewen Hu
- 2College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 People's Republic of China
| | - Xingyu Wang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
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Onishi A, Takano K, Kawase T, Ora H, Kansaku K. Affective Stimuli for an Auditory P300 Brain-Computer Interface. Front Neurosci 2017; 11:522. [PMID: 28983235 PMCID: PMC5613193 DOI: 10.3389/fnins.2017.00522] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 09/05/2017] [Indexed: 12/04/2022] Open
Abstract
Gaze-independent brain computer interfaces (BCIs) are a potential communication tool for persons with paralysis. This study applies affective auditory stimuli to investigate their effects using a P300 BCI. Fifteen able-bodied participants operated the P300 BCI, with positive and negative affective sounds (PA: a meowing cat sound, NA: a screaming cat sound). Permuted stimuli of the positive and negative affective sounds (permuted-PA, permuted-NA) were also used for comparison. Electroencephalography data was collected, and offline classification accuracies were compared. We used a visual analog scale (VAS) to measure positive and negative affective feelings in the participants. The mean classification accuracies were 84.7% for PA and 67.3% for permuted-PA, while the VAS scores were 58.5 for PA and −12.1 for permuted-PA. The positive affective stimulus showed significantly higher accuracy and VAS scores than the negative affective stimulus. In contrast, mean classification accuracies were 77.3% for NA and 76.0% for permuted-NA, while the VAS scores were −50.0 for NA and −39.2 for permuted NA, which are not significantly different. We determined that a positive affective stimulus with accompanying positive affective feelings significantly improved BCI accuracy. Additionally, an ALS patient achieved 90% online classification accuracy. These results suggest that affective stimuli may be useful for preparing a practical auditory BCI system for patients with disabilities.
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Affiliation(s)
- Akinari Onishi
- Systems Neuroscience Section, Department of Rehabilitation for Brain Function, Research Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawa, Japan.,Center for Frontier Medical Engineering, Chiba UniversityInage, Japan
| | - Kouji Takano
- Systems Neuroscience Section, Department of Rehabilitation for Brain Function, Research Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawa, Japan
| | - Toshihiro Kawase
- Systems Neuroscience Section, Department of Rehabilitation for Brain Function, Research Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawa, Japan.,Biointerfaces Unit, Institute of Innovative Research, Tokyo Institute of TechnologyYokohama, Japan
| | - Hiroki Ora
- Systems Neuroscience Section, Department of Rehabilitation for Brain Function, Research Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawa, Japan.,Brain Science Inspired Life Support Research Center, The University of Electro-CommunicationsChofu, Japan
| | - Kenji Kansaku
- Systems Neuroscience Section, Department of Rehabilitation for Brain Function, Research Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawa, Japan.,Brain Science Inspired Life Support Research Center, The University of Electro-CommunicationsChofu, Japan
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17
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Cao Y, An X, Ke Y, Jiang J, Yang H, Chen Y, Jiao X, Qi H, Ming D. The effects of semantic congruency: a research of audiovisual P300-speller. Biomed Eng Online 2017; 16:91. [PMID: 28743262 PMCID: PMC5526248 DOI: 10.1186/s12938-017-0381-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 07/18/2017] [Indexed: 11/23/2022] Open
Abstract
Background Over the past few decades, there have been many studies of aspects of brain–computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm. Methods The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms. Results The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm. Conclusions In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.
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Affiliation(s)
- Yong Cao
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Xingwei An
- Department of Biomedical Engineering, Tianjin University, Tianjin, China.
| | - Yufeng Ke
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Jin Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Hanjun Yang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Yuqian Chen
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Xuejun Jiao
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Hongzhi Qi
- Department of Biomedical Engineering, Tianjin University, Tianjin, China.
| | - Dong Ming
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
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18
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An X, Wei J, Liu S, Ming D. A sLORETA study for gaze-independent BCI speller. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:994-997. [PMID: 29060041 DOI: 10.1109/embc.2017.8036993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
EEG-based BCI (brain-computer-interface) speller, especially gaze-independent BCI speller, has become a hot topic in recent years. It provides direct spelling device by non-muscular method for people with severe motor impairments and with limited gaze movement. Brain needs to conduct both stimuli-driven and stimuli-related attention in fast presented BCI paradigms for such BCI speller applications. Few researchers studied the mechanism of brain response to such fast presented BCI applications. In this study, we compared the distribution of brain activation in visual, auditory, and audio-visual combined stimuli paradigms using sLORETA (standardized low-resolution brain electromagnetic tomography). Between groups comparisons showed the importance of visual and auditory stimuli in audio-visual combined paradigm. They both contribute to the activation of brain regions, with visual stimuli being the predominate stimuli. Visual stimuli related brain region was mainly located at parietal and occipital lobe, whereas response in frontal-temporal lobes might be caused by auditory stimuli. These regions played an important role in audio-visual bimodal paradigms. These new findings are important for future study of ERP speller as well as the mechanism of fast presented stimuli.
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Heo J, Baek HJ, Hong S, Chang MH, Lee JS, Park KS. Music and natural sounds in an auditory steady-state response based brain-computer interface to increase user acceptance. Comput Biol Med 2017; 84:45-52. [PMID: 28342407 DOI: 10.1016/j.compbiomed.2017.03.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 03/15/2017] [Indexed: 11/16/2022]
Abstract
Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain-computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy.
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Affiliation(s)
- Jeong Heo
- Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Hyun Jae Baek
- Mobile Communication Business, Samsung Electronics Co., Ltd., Suwon, Republic of Korea
| | - Seunghyeok Hong
- Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Min Hye Chang
- Advanced Medical Device Research Division, Korea Electro-Technology Research Institute, Ansan, Republic of Korea
| | - Jeong Su Lee
- Mobile Communication Business, Samsung Electronics Co., Ltd., Suwon, Republic of Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea.
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20
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Erlbeck H, Mochty U, Kübler A, Real RGL. Circadian course of the P300 ERP in patients with amyotrophic lateral sclerosis - implications for brain-computer interfaces (BCI). BMC Neurol 2017; 17:3. [PMID: 28061886 PMCID: PMC5219734 DOI: 10.1186/s12883-016-0782-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 12/09/2016] [Indexed: 12/14/2022] Open
Abstract
Background Accidents or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) can lead to progressing, extensive, and complete paralysis leaving patients aware but unable to communicate (locked-in state). Brain-computer interfaces (BCI) based on electroencephalography represent an important approach to establish communication with these patients. The most common BCI for communication rely on the P300, a positive deflection arising in response to rare events. To foster broader application of BCIs for restoring lost function, also for end-users with impaired vision, we explored whether there were specific time windows during the day in which a P300 driven BCI should be preferably applied. Methods The present study investigated the influence of time of the day and modality (visual vs. auditory) on P300 amplitude and latency. A sample of 14 patients (end-users) with ALS and 14 healthy age matched volunteers participated in the study and P300 event-related potentials (ERP) were recorded at four different times (10, 12 am, 2, & 4 pm) during the day. Results Results indicated no differences in P300 amplitudes or latencies between groups (ALS patients v. healthy participants) or time of measurement. In the auditory condition, latencies were shorter and amplitudes smaller as compared to the visual condition. Conclusion Our findings suggest applicability of EEG/BCI sessions in patients with ALS throughout normal waking hours. Future studies using actual BCI systems are needed to generalize these findings with regard to BCI effectiveness/efficiency and other times of day.
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Affiliation(s)
- Helena Erlbeck
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Ursula Mochty
- Institute of Medical Psychology, University of Tübingen, Tübingen, Germany
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Ruben G L Real
- Institute of Psychology, University of Würzburg, Würzburg, Germany. .,Institute of Medical Psychology and Medical Sociology, University Medical Center Göttingen, Waldweg 37, 37073, Göttingen, Germany.
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21
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Barbosa S, Pires G, Nunes U. Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli. J Neurosci Methods 2016; 261:47-61. [DOI: 10.1016/j.jneumeth.2015.11.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Revised: 11/26/2015] [Accepted: 11/26/2015] [Indexed: 12/13/2022]
Affiliation(s)
- Sara Barbosa
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal.
| | - Gabriel Pires
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Engineering, Polytechnic Institute of Tomar, Tomar, Portugal.
| | - Urbano Nunes
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal.
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22
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An X, Tang J, Liu S, He F, Qi H, Wan B, Ming D. Effects of Temporal Congruity Between Auditory and Visual Stimuli Using Rapid Audio-Visual Serial Presentation. IEEE Trans Biomed Eng 2016; 63:2125-32. [PMID: 26841382 DOI: 10.1109/tbme.2015.2511539] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
GOAL Combining visual and auditory stimuli in event-related potential (ERP)-based spellers gained more attention in recent years. Few of these studies notice the difference of ERP components and system efficiency caused by the shifting of visual and auditory onset. Here, we aim to study the effect of temporal congruity of auditory and visual stimuli onset on bimodal brain-computer interface (BCI) speller. METHODS We designed five visual and auditory combined paradigms with different visual-to-auditory delays (-33 to +100 ms). Eleven participants attended in this study. ERPs were acquired and aligned according to visual and auditory stimuli onset, respectively. ERPs of Fz, Cz, and PO7 channels were studied through the statistical analysis of different conditions both from visual-aligned ERPs and audio-aligned ERPs. Based on the visual-aligned ERPs, classification accuracy was also analyzed to seek the effects of visual-to-auditory delays. RESULTS The latencies of ERP components depended mainly on the visual stimuli onset. Auditory stimuli onsets influenced mainly on early component accuracies, whereas visual stimuli onset determined later component accuracies. The latter, however, played a dominate role in overall classification. SIGNIFICANCE This study is important for further studies to achieve better explanations and ultimately determine the way to optimize the bimodal BCI application.
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23
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Halder S, Käthner I, Kübler A. Training leads to increased auditory brain–computer interface performance of end-users with motor impairments. Clin Neurophysiol 2016; 127:1288-1296. [DOI: 10.1016/j.clinph.2015.08.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 07/16/2015] [Accepted: 08/05/2015] [Indexed: 11/28/2022]
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Treder MS, Porbadnigk AK, Shahbazi Avarvand F, Müller KR, Blankertz B. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis. Neuroimage 2016; 129:279-291. [PMID: 26804780 DOI: 10.1016/j.neuroimage.2016.01.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 01/08/2016] [Accepted: 01/09/2016] [Indexed: 10/22/2022] Open
Abstract
We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.
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Affiliation(s)
- Matthias S Treder
- Neurotechnology Group, Technische Universität Berlin, Germany; Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, UK.
| | | | | | - Klaus-Robert Müller
- Machine Learning Laboratory, Technische Universität Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
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Höhne J, Bartz D, Hebart MN, Müller KR, Blankertz B. Analyzing neuroimaging data with subclasses: A shrinkage approach. Neuroimage 2016; 124:740-751. [DOI: 10.1016/j.neuroimage.2015.09.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 09/10/2015] [Accepted: 09/15/2015] [Indexed: 11/30/2022] Open
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Kleih SC, Herweg A, Kaufmann T, Staiger-Sälzer P, Gerstner N, Kübler A. The WIN-speller: a new intuitive auditory brain-computer interface spelling application. Front Neurosci 2015; 9:346. [PMID: 26500476 PMCID: PMC4594437 DOI: 10.3389/fnins.2015.00346] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 09/14/2015] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to test the usability of a new auditory Brain-Computer Interface (BCI) application for communication. We introduce a word based, intuitive auditory spelling paradigm the WIN-speller. In the WIN-speller letters are grouped by words, such as the word KLANG representing the letters A, G, K, L, and N. Thereby, the decoding step between perceiving a code and translating it to the stimuli it represents becomes superfluous. We tested 11 healthy volunteers and four end-users with motor impairment in the copy spelling mode. Spelling was successful with an average accuracy of 84% in the healthy sample. Three of the end-users communicated with average accuracies of 80% or higher while one user was not able to communicate reliably. Even though further evaluation is required, the WIN-speller represents a potential alternative for BCI based communication in end-users.
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Affiliation(s)
- Sonja C Kleih
- Department of Psychology, University of Würzburg Würzburg, Germany
| | - Andreas Herweg
- Department of Psychology, University of Würzburg Würzburg, Germany
| | - Tobias Kaufmann
- KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo Oslo, Norway
| | - Pit Staiger-Sälzer
- Rehabilitationszentrum Bethesda, Beratungsstelle für Unterstützte Kommunikation Bad Kreuznach, Germany
| | | | - Andrea Kübler
- Department of Psychology, University of Würzburg Würzburg, Germany
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Baykara E, Ruf CA, Fioravanti C, Käthner I, Simon N, Kleih SC, Kübler A, Halder S. Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol 2015; 127:379-387. [PMID: 26051753 DOI: 10.1016/j.clinph.2015.04.054] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Revised: 03/05/2015] [Accepted: 04/01/2015] [Indexed: 11/19/2022]
Abstract
OBJECTIVES Brain-computer interface (BCI) technology aims at helping end-users with severe motor paralysis to communicate with their environment without using the natural output pathways of the brain. For end-users in complete paralysis, loss of gaze control may necessitate non-visual BCI systems. The present study investigated the effect of training on performance with an auditory P300 multi-class speller paradigm. For half of the participants, spatial cues were added to the auditory stimuli to see whether performance can be further optimized. The influence of motivation, mood and workload on performance and P300 component was also examined. METHODS In five sessions, 16 healthy participants were instructed to spell several words by attending to animal sounds representing the rows and columns of a 5 × 5 letter matrix. RESULTS 81% of the participants achieved an average online accuracy of ⩾ 70%. From the first to the fifth session information transfer rates increased from 3.72 bits/min to 5.63 bits/min. Motivation significantly influenced P300 amplitude and online ITR. No significant facilitative effect of spatial cues on performance was observed. CONCLUSIONS Training improves performance in an auditory BCI paradigm. Motivation influences performance and P300 amplitude. SIGNIFICANCE The described auditory BCI system may help end-users to communicate independently of gaze control with their environment.
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Affiliation(s)
- E Baykara
- Institute of Psychology, University of Würzburg, Marcusstrasse 9-11, 97070 Würzburg, Germany.
| | - C A Ruf
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany
| | - C Fioravanti
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany
| | - I Käthner
- Institute of Psychology, University of Würzburg, Marcusstrasse 9-11, 97070 Würzburg, Germany
| | - N Simon
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany
| | - S C Kleih
- Institute of Psychology, University of Würzburg, Marcusstrasse 9-11, 97070 Würzburg, Germany
| | - A Kübler
- Institute of Psychology, University of Würzburg, Marcusstrasse 9-11, 97070 Würzburg, Germany.
| | - S Halder
- Institute of Psychology, University of Würzburg, Marcusstrasse 9-11, 97070 Würzburg, Germany; Department of Rehabilitation for Brain Functions, Research Institute of National Rehabilitation Center for Persons with Disabilities, Tokorozawa, Saitama 359-8555, Japan.
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28
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Simon N, Käthner I, Ruf CA, Pasqualotto E, Kübler A, Halder S. An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user. Front Hum Neurosci 2015; 8:1039. [PMID: 25620924 PMCID: PMC4288388 DOI: 10.3389/fnhum.2014.01039] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 12/11/2014] [Indexed: 11/18/2022] Open
Abstract
Brain-computer interfaces (BCIs) can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g., in the completely locked-in state) or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on 2 consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS) in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min) that increased to 90% (4.23 bits/min) on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.
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Affiliation(s)
- Nadine Simon
- Institute of Medical Psychology and Behavioral Neurobiology, University of TübingenTübingen, Germany
- Max Planck Institute for Intelligent SystemsTübingen, Germany
| | - Ivo Käthner
- Institute of Psychology, University of WürzburgWürzburg, Germany
| | - Carolin A. Ruf
- Institute of Medical Psychology and Behavioral Neurobiology, University of TübingenTübingen, Germany
| | - Emanuele Pasqualotto
- Psychological Sciences Research Institute, Université Catholique de LouvainLouvain-la-Neuve, Belgium
| | - Andrea Kübler
- Institute of Psychology, University of WürzburgWürzburg, Germany
| | - Sebastian Halder
- Institute of Psychology, University of WürzburgWürzburg, Germany
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29
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An X, Höhne J, Ming D, Blankertz B. Exploring combinations of auditory and visual stimuli for gaze-independent brain-computer interfaces. PLoS One 2014; 9:e111070. [PMID: 25350547 PMCID: PMC4211702 DOI: 10.1371/journal.pone.0111070] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Accepted: 09/20/2014] [Indexed: 12/03/2022] Open
Abstract
For Brain-Computer Interface (BCI) systems that are designed for users with severe impairments of the oculomotor system, an appropriate mode of presenting stimuli to the user is crucial. To investigate whether multi-sensory integration can be exploited in the gaze-independent event-related potentials (ERP) speller and to enhance BCI performance, we designed a visual-auditory speller. We investigate the possibility to enhance stimulus presentation by combining visual and auditory stimuli within gaze-independent spellers. In this study with N = 15 healthy users, two different ways of combining the two sensory modalities are proposed: simultaneous redundant streams (Combined-Speller) and interleaved independent streams (Parallel-Speller). Unimodal stimuli were applied as control conditions. The workload, ERP components, classification accuracy and resulting spelling speed were analyzed for each condition. The Combined-speller showed a lower workload than uni-modal paradigms, without the sacrifice of spelling performance. Besides, shorter latencies, lower amplitudes, as well as a shift of the temporal and spatial distribution of discriminative information were observed for Combined-speller. These results are important and are inspirations for future studies to search the reason for these differences. For the more innovative and demanding Parallel-Speller, where the auditory and visual domains are independent from each other, a proof of concept was obtained: fifteen users could spell online with a mean accuracy of 87.7% (chance level <3%) showing a competitive average speed of 1.65 symbols per minute. The fact that it requires only one selection period per symbol makes it a good candidate for a fast communication channel. It brings a new insight into the true multisensory stimuli paradigms. Novel approaches for combining two sensory modalities were designed here, which are valuable for the development of ERP-based BCI paradigms.
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Affiliation(s)
- Xingwei An
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
- Neurotechnology Group, Berlin Institute of Technology, Berlin, Germany
- * E-mail: (XA); (DM)
| | - Johannes Höhne
- Neurotechnology Group, Berlin Institute of Technology, Berlin, Germany
- Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
| | - Dong Ming
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
- * E-mail: (XA); (DM)
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Höhne J, Holz E, Staiger-Sälzer P, Müller KR, Kübler A, Tangermann M. Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution. PLoS One 2014; 9:e104854. [PMID: 25162231 PMCID: PMC4146550 DOI: 10.1371/journal.pone.0104854] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 07/11/2014] [Indexed: 11/23/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.
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Affiliation(s)
- Johannes Höhne
- Neurotechnology group, Berlin Institute of Technology, Berlin, Germany
| | - Elisa Holz
- Department of Psychology I, University of Würzburg, Würzburg, Germany
| | - Pit Staiger-Sälzer
- Beratungsstelle für Unterstützte Kommunikation (BUK), Diakonie Bad Kreuznach, Bad Kreuznach, Germany
| | - Klaus-Robert Müller
- Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea
| | - Andrea Kübler
- Department of Psychology I, University of Würzburg, Würzburg, Germany
| | - Michael Tangermann
- BrainLinks-BrainTools Excellence Cluster, University of Freiburg, Freiburg, Germany
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Kindermans PJ, Schreuder M, Schrauwen B, Müller KR, Tangermann M. True zero-training brain-computer interfacing--an online study. PLoS One 2014; 9:e102504. [PMID: 25068464 PMCID: PMC4113217 DOI: 10.1371/journal.pone.0102504] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 06/19/2014] [Indexed: 11/18/2022] Open
Abstract
Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.
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Affiliation(s)
- Pieter-Jan Kindermans
- Electronics and Information Systems (ELIS) Dept., Ghent University, Ghent, Belgium
- * E-mail: (PJK); (KRM); (MT)
| | - Martijn Schreuder
- Machine Learning Laboratory, Technical University of Berlin, Berlin, Germany
| | - Benjamin Schrauwen
- Electronics and Information Systems (ELIS) Dept., Ghent University, Ghent, Belgium
| | - Klaus-Robert Müller
- Machine Learning Laboratory, Technical University of Berlin, Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
- * E-mail: (PJK); (KRM); (MT)
| | - Michael Tangermann
- BrainLinks-BrainTools Excellence Cluster, Computer Science Dept., University of Freiburg, Freiburg, Germany
- * E-mail: (PJK); (KRM); (MT)
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Höhne J, Tangermann M. Towards user-friendly spelling with an auditory brain-computer interface: the CharStreamer paradigm. PLoS One 2014; 9:e98322. [PMID: 24886978 PMCID: PMC4041754 DOI: 10.1371/journal.pone.0098322] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Accepted: 04/30/2014] [Indexed: 11/18/2022] Open
Abstract
Realizing the decoding of brain signals into control commands, brain-computer interfaces (BCI) aim to establish an alternative communication pathway for locked-in patients. In contrast to most visual BCI approaches which use event-related potentials (ERP) of the electroencephalogram, auditory BCI systems are challenged with ERP responses, which are less class-discriminant between attended and unattended stimuli. Furthermore, these auditory approaches have more complex interfaces which imposes a substantial workload on their users. Aiming for a maximally user-friendly spelling interface, this study introduces a novel auditory paradigm: "CharStreamer". The speller can be used with an instruction as simple as "please attend to what you want to spell". The stimuli of CharStreamer comprise 30 spoken sounds of letters and actions. As each of them is represented by the sound of itself and not by an artificial substitute, it can be selected in a one-step procedure. The mental mapping effort (sound stimuli to actions) is thus minimized. Usability is further accounted for by an alphabetical stimulus presentation: contrary to random presentation orders, the user can foresee the presentation time of the target letter sound. Healthy, normal hearing users (n = 10) of the CharStreamer paradigm displayed ERP responses that systematically differed between target and non-target sounds. Class-discriminant features, however, varied individually from the typical N1-P2 complex and P3 ERP components found in control conditions with random sequences. To fully exploit the sequential presentation structure of CharStreamer, novel data analysis approaches and classification methods were introduced. The results of online spelling tests showed that a competitive spelling speed can be achieved with CharStreamer. With respect to user rating, it clearly outperforms a control setup with random presentation sequences.
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Affiliation(s)
- Johannes Höhne
- Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany
- Neurotechnology group, Berlin Institute of Technology, Berlin, Germany
| | - Michael Tangermann
- BrainLinks-BrainTools Excellence Cluster, University of Freiburg, Freiburg, Germany
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Hill NJ, Ricci E, Haider S, McCane LM, Heckman S, Wolpaw JR, Vaughan TM. A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. J Neural Eng 2014; 11:035003. [PMID: 24838278 PMCID: PMC4096243 DOI: 10.1088/1741-2560/11/3/035003] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state. APPROACH We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. MAIN RESULTS The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. SIGNIFICANCE Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.
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Affiliation(s)
- N. Jeremy Hill
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
- Helen Hayes Hospital (West Haverstraw, NY, USA)
| | - Erin Ricci
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
| | - Sameah Haider
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
- Albany Medical College (Albany, NY, USA)
| | - Lynn M. McCane
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
| | - Susan Heckman
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
| | - Jonathan R. Wolpaw
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
- University at Albany, State University of New York (Albany, NY, USA)
| | - Theresa M. Vaughan
- Wadsworth Center, New York State Department of Health (Albany, NY,
USA)
- Helen Hayes Hospital (West Haverstraw, NY, USA)
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Treder MS, Purwins H, Miklody D, Sturm I, Blankertz B. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. J Neural Eng 2014; 11:026009. [PMID: 24608228 DOI: 10.1088/1741-2560/11/2/026009] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Sella I, Reiner M, Pratt H. Natural stimuli from three coherent modalities enhance behavioral responses and electrophysiological cortical activity in humans. Int J Psychophysiol 2013; 93:45-55. [PMID: 24315926 DOI: 10.1016/j.ijpsycho.2013.11.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Revised: 10/23/2013] [Accepted: 11/26/2013] [Indexed: 11/15/2022]
Abstract
Cues that involve a number of sensory modalities are processed in the brain in an interactive multimodal manner rather than independently for each modality. We studied multimodal integration in a natural, yet fully controlled scene, implemented as an interactive game in an auditory-haptic-visual virtual environment. In this imitation of a natural scene, the targets of perception were ecologically valid uni-, bi- and tri-modal manifestations of a simple event-a ball hitting a wall. Subjects were engaged in the game while their behavioral and early cortical electrophysiological responses were measured. Behavioral results confirmed that tri-modal cues were detected faster and more accurately than bi-modal cues, which, likewise, showed advantages over unimodal responses. Event-Related Potentials (ERPs) were recorded, and the first 200 ms following stimulus onset was analyzed to reveal the latencies of cortical multimodal interactions as estimated by sLORETA. These electrophysiological findings indicated bi-modal as well as tri-modal interactions beginning very early (~30 ms), uniquely for each multimodal combination. The results suggest that early cortical multimodal integration accelerates cortical activity and, in turn, enhances performance measures. This acceleration registers on the scalp as sub-additive cortical activation.
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Affiliation(s)
- Irit Sella
- The Virtual Reality and NeuroCognition Laboratory, Technion, Israel Institute of Science, Israel; Evoked Potentials Laboratory, Technion, Israel Institute of Science, Israel
| | - Miriam Reiner
- The Virtual Reality and NeuroCognition Laboratory, Technion, Israel Institute of Science, Israel.
| | - Hillel Pratt
- Evoked Potentials Laboratory, Technion, Israel Institute of Science, Israel
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Holz EM, Höhne J, Staiger-Sälzer P, Tangermann M, Kübler A. Brain–computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users. Artif Intell Med 2013; 59:111-20. [PMID: 24080080 DOI: 10.1016/j.artmed.2013.08.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2012] [Revised: 08/11/2013] [Accepted: 08/16/2013] [Indexed: 11/20/2022]
Affiliation(s)
- Elisa Mira Holz
- Institute of Psychology, University of Würzburg, Marcusstr. 9-11, 97070 Würzburg, Germany.
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Yaming Xu, Nakajima Y. A Two-Level Predictive Event-Related Potential-Based Brain–Computer Interface. IEEE Trans Biomed Eng 2013; 60:2839-47. [DOI: 10.1109/tbme.2013.2265103] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Kübler A, Mattia D, Rupp R, Tangermann M. Facing the challenge: bringing brain-computer interfaces to end-users. Artif Intell Med 2013; 59:55-60. [PMID: 24076343 DOI: 10.1016/j.artmed.2013.08.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 08/16/2013] [Indexed: 10/26/2022]
Affiliation(s)
- Andrea Kübler
- Institute of Psychology, University of Würzburg, Marcusstr. 9-11, 97070 Würzburg, Germany.
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Höhne J, Tangermann M. How stimulation speed affects Event-Related Potentials and BCI performance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:1802-5. [PMID: 23366261 DOI: 10.1109/embc.2012.6346300] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In most paradigms for Brain-Computer Interfaces (BCIs) that are based on Event-Related Potentials (ERPs), stimuli are presented with a pre-defined and constant speed. In order to boost BCI performance by optimizing the parameters of stimulation, this offline study investigates the impact of the stimulus onset asynchrony (SOA) on ERPs and the resulting classification accuracy. The SOA is defined as the time between the onsets of two consecutive stimuli, which represents a measure for stimulation speed. A simple auditory oddball paradigm was tested in 14 SOA conditions with a SOA between 50 ms and 1000 ms. Based on an offline ERP analysis, the BCI performance (quantified by the Information Transfer Rate, ITR in bits/min) was simulated. A great variability in the simulated BCI performance was observed within subjects (N=11). This indicates a potential increase in BCI performance (≥ 1.6 bits/min) for ERP-based paradigms, if the stimulation speed is specified for each user individually.
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Affiliation(s)
- Johannes Höhne
- Machine Learning Department, Berlin Institute of Technology, Berlin, Germany.
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Brandmeyer A, Farquhar JDR, McQueen JM, Desain PWM. Decoding speech perception by native and non-native speakers using single-trial electrophysiological data. PLoS One 2013; 8:e68261. [PMID: 23874567 PMCID: PMC3708957 DOI: 10.1371/journal.pone.0068261] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Accepted: 05/27/2013] [Indexed: 11/19/2022] Open
Abstract
Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.
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Affiliation(s)
- Alex Brandmeyer
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
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Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M. Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng 2013; 10:036025. [PMID: 23685458 DOI: 10.1088/1741-2560/10/3/036025] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In brain-computer interface (BCI) research, systems based on event-related potentials (ERP) are considered particularly successful and robust. This stems in part from the repeated stimulation which counteracts the low signal-to-noise ratio in electroencephalograms. Repeated stimulation leads to an optimization problem, as more repetitions also cost more time. The optimal number of repetitions thus represents a data-dependent trade-off between the stimulation time and the obtained accuracy. Several methods for dealing with this have been proposed as 'early stopping', 'dynamic stopping' or 'adaptive stimulation'. Despite their high potential for BCI systems at the patient's bedside, those methods are typically ignored in current BCI literature. The goal of the current study is to assess the benefit of these methods. APPROACH This study assesses for the first time the existing methods on a common benchmark of both artificially generated data and real BCI data of 83 BCI sessions, allowing for a direct comparison between these methods in the context of text entry. MAIN RESULTS The results clearly show the beneficial effect on the online performance of a BCI system, if the trade-off between the number of stimulus repetitions and accuracy is optimized. All assessed methods work very well for data of good subjects, and worse for data of low-performing subjects. Most methods, however, are robust in the sense that they do not reduce the performance below the baseline of a simple no stopping strategy. SIGNIFICANCE Since all methods can be realized as a module between the BCI and an application, minimal changes are needed to include these methods into existing BCI software architectures. Furthermore, the hyperparameters of most methods depend to a large extend on only a single variable-the discriminability of the training data. For the convenience of BCI practitioners, the present study proposes linear regression coefficients for directly estimating the hyperparameters from the data based on this discriminability. The data that were used in this publication are made publicly available to benchmark future methods.
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Geuze J, van Gerven MAJ, Farquhar J, Desain P. Detecting semantic priming at the single-trial level. PLoS One 2013; 8:e60377. [PMID: 23565237 PMCID: PMC3615017 DOI: 10.1371/journal.pone.0060377] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2012] [Accepted: 02/26/2013] [Indexed: 11/19/2022] Open
Abstract
Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N = 12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.
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Affiliation(s)
- Jeroen Geuze
- Radboud University Nijmegen, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands.
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Baek HJ, Kim HS, Heo J, Lim YG, Park KS. Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses. J Neural Eng 2013; 10:024001. [PMID: 23448913 DOI: 10.1088/1741-2560/10/2/024001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. APPROACH A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. MAIN RESULTS Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. SIGNIFICANCE The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.
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Affiliation(s)
- Hyun Jae Baek
- Graduate Program in Bioengineering, Seoul National University, Seoul 110-799, Korea
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Nambu I, Ebisawa M, Kogure M, Yano S, Hokari H, Wada Y. Estimating the intended sound direction of the user: toward an auditory brain-computer interface using out-of-head sound localization. PLoS One 2013; 8:e57174. [PMID: 23437338 PMCID: PMC3577758 DOI: 10.1371/journal.pone.0057174] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2012] [Accepted: 01/18/2013] [Indexed: 11/18/2022] Open
Abstract
The auditory Brain-Computer Interface (BCI) using electroencephalograms (EEG) is a subject of intensive study. As a cue, auditory BCIs can deal with many of the characteristics of stimuli such as tone, pitch, and voices. Spatial information on auditory stimuli also provides useful information for a BCI. However, in a portable system, virtual auditory stimuli have to be presented spatially through earphones or headphones, instead of loudspeakers. We investigated the possibility of an auditory BCI using the out-of-head sound localization technique, which enables us to present virtual auditory stimuli to users from any direction, through earphones. The feasibility of a BCI using this technique was evaluated in an EEG oddball experiment and offline analysis. A virtual auditory stimulus was presented to the subject from one of six directions. Using a support vector machine, we were able to classify whether the subject attended the direction of a presented stimulus from EEG signals. The mean accuracy across subjects was 70.0% in the single-trial classification. When we used trial-averaged EEG signals as inputs to the classifier, the mean accuracy across seven subjects reached 89.5% (for 10-trial averaging). Further analysis showed that the P300 event-related potential responses from 200 to 500 ms in central and posterior regions of the brain contributed to the classification. In comparison with the results obtained from a loudspeaker experiment, we confirmed that stimulus presentation by out-of-head sound localization achieved similar event-related potential responses and classification performances. These results suggest that out-of-head sound localization enables us to provide a high-performance and loudspeaker-less portable BCI system.
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Affiliation(s)
- Isao Nambu
- Nagaoka University of Technology, Nagaoka, Niigata, Japan
| | | | - Masumi Kogure
- Nagaoka University of Technology, Nagaoka, Niigata, Japan
| | - Shohei Yano
- Nagaoka National College of Technology, Nagaoka, Niigata, Japan
| | | | - Yasuhiro Wada
- Nagaoka University of Technology, Nagaoka, Niigata, Japan
- * E-mail:
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Tangermann M, Hohne J, Stecher H, Schreuder M. No surprise--sequence event-related potentials for brain-computer interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:2501-2504. [PMID: 23366433 DOI: 10.1109/embc.2012.6346472] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
INTRODUCTION In the field of Brain-Computer Interfaces (BCI), the original two-class oddball paradigm has been extended to multiple stimuli with balanced probabilities and random presentation sequences. Exploiting the differences between standard and deviant ERP responses, these multi-class paradigms are suitable for communication and control. METHODS The present study investigates the effect of giving up the randomness of stimulation sequences in favor of a repeated, predictable pattern. Data of healthy subjects (n=10) who performed a single session with a 6-class spatial auditory ERP paradigm were analyzed offline. Their auditory evoked potentials (AEP) resulting from the potentially simpler task (using fixed sequences) are compared with the AEP evoked by pseudo-randomized stimulation sequences. RESULTS Class-discriminative EEG responses between target and non-target stimuli were observed for both conditions. The binary classification error estimated for standard epochs of was comparable for both conditions (random: 24%, fixed: 25%). Expanding the standard epochs to include pre-stimulus intervals, we found that the regular structure of the fixed sequence can be exploited. Compared to the standard epoch, the MSE improves by 7%, while in the random condition an improvement could not be observed.
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Affiliation(s)
- Michael Tangermann
- Dept. Machine Learning, Berlin Institute of Technology (TU Berlin), Berlin, Germany. michael.tangermann at tu-berlin.de
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