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Hramov AE, Maksimenko V, Koronovskii A, Runnova AE, Zhuravlev M, Pisarchik AN, Kurths J. Percept-related EEG classification using machine learning approach and features of functional brain connectivity. CHAOS (WOODBURY, N.Y.) 2019; 29:093110. [PMID: 31575147 DOI: 10.1063/1.5113844] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g., principal component analysis, linear discriminant analysis, etc.) regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. If so, it is possible to specify the set of EEG principal features based on the prior knowledge about underlying neurophysiological processes. To test this hypothesis, we consider the classification of EEG trials related to the perception of ambiguous visual stimuli. We observe that EEG features, underlying the different ambiguous stimuli interpretations, are defined by the network properties of neuronal activity. Having analyzed functional neural interactions, we specify the brain area in which neural network architecture exhibits differences for different classes of EEG trials. We optimize the feedforward multilayer perceptron and develop a strategy for the training set selection to maximize the classification accuracy, being 85% when all channels are used. The revealed localization of the percept-related features allows about 95% accuracy, when the number of channels is reduced up to 90%. Obtained results can be used for classification of EEG trials associated with more complex cognitive tasks. Taking into account that cognitive activity is subserved by a distributed functional cortical network, its topological properties have to be considered when selecting optimal features for EEG trial classification.
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Affiliation(s)
- Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Vladimir Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Alexey Koronovskii
- Faculty of Nonlinear Processes, Saratov State University, 410012 Saratov, Russia
| | - Anastasiya E Runnova
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Maxim Zhuravlev
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Alexander N Pisarchik
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
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Hramov AE, Maksimenko VA, Pchelintseva SV, Runnova AE, Grubov VV, Musatov VY, Zhuravlev MO, Koronovskii AA, Pisarchik AN. Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks. Front Neurosci 2017; 11:674. [PMID: 29255403 PMCID: PMC5722852 DOI: 10.3389/fnins.2017.00674] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/20/2017] [Indexed: 01/04/2023] Open
Abstract
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
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Affiliation(s)
- Alexander E Hramov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Vladimir A Maksimenko
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Svetlana V Pchelintseva
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Anastasiya E Runnova
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vadim V Grubov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vyacheslav Yu Musatov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Maksim O Zhuravlev
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexey A Koronovskii
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexander N Pisarchik
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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Alotaiby T, El-Samie FEA, Alshebeili SA, Ahmad I. A review of channel selection algorithms for EEG signal processing. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2015; 2015:66. [DOI: 10.1186/s13634-015-0251-9] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Accepted: 07/15/2015] [Indexed: 09/02/2023]
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Kirschner A, Cruse D, Chennu S, Owen AM, Hampshire A. A P300-based cognitive assessment battery. Brain Behav 2015; 5:e00336. [PMID: 26085962 PMCID: PMC4467771 DOI: 10.1002/brb3.336] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 02/15/2015] [Accepted: 02/25/2015] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND It is well established that some patients who are diagnosed as being in a vegetative state or a minimally conscious state show reliable signs of volition that may only be detected by measuring neural responses. A pertinent question is whether these patients are capable of higher cognitive processes. METHODS Here, we develop a series of EEG paradigms that probe several core aspects of cognition at the bedside without the need for motor responses and explore the sensitivity of this approach in a group of healthy controls. RESULTS Using analysis of ERPs alone, this method can determine with high reliability whether individual participants are able to attend a stimulus stream, maintain items in working memory, or solve complex grammatical reasoning problems. CONCLUSION We suggest that this approach could form the basis of a brain-based battery for assessing higher cognition in patients with severe motor impairments or disorders of consciousness.
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Affiliation(s)
- Aaron Kirschner
- The Brain and Mind Institute, University of Western OntarioLondon, Ontario, Canada
| | - Damian Cruse
- The Brain and Mind Institute, University of Western OntarioLondon, Ontario, Canada
| | - Srivas Chennu
- Department of Clinical Neurosciences, The University of CambridgeCambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, University of Western OntarioLondon, Ontario, Canada
| | - Adam Hampshire
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College LondonLondon, UK
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Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness. PLoS One 2013; 8:e80479. [PMID: 24282545 PMCID: PMC3839976 DOI: 10.1371/journal.pone.0080479] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Accepted: 10/03/2013] [Indexed: 12/01/2022] Open
Abstract
Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.
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Toward a model-based predictive controller design in brain-computer interfaces. Ann Biomed Eng 2011; 39:1482-92. [PMID: 21267657 DOI: 10.1007/s10439-011-0248-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 08/20/2010] [Indexed: 10/18/2022]
Abstract
A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.
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Kamrunnahar M, Dias NS, Schiff SJ. Optimization of electrode channels 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 2010; 2009:6477-80. [PMID: 19964437 DOI: 10.1109/iembs.2009.5333585] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.
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Affiliation(s)
- M Kamrunnahar
- Center for Neural Engineering, Dept. of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, USA.
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Kamrunnahar M, Dias NS, Schiff SJ, Gluckman BJ. Model-based responses and features 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 2009; 2008:4482-5. [PMID: 19163711 DOI: 10.1109/iembs.2008.4650208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%-30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.
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Affiliation(s)
- M Kamrunnahar
- Dept. of Engineering Sciences and Mechanics, The Pennsylvania State University, University Park, 16802, USA.
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