Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface
The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must c...
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Published in | Computer methods and programs in biomedicine Vol. 244; p. 107944 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Abstract | The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features.
In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME).
Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks.
These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals. |
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AbstractList | The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features.
In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME).
Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks.
These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals. BACKGROUND AND OBJECTIVEThe brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features.METHODSIn the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME).RESULTSOur results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks.CONCLUSIONSThese results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals. |
ArticleNumber | 107944 |
Author | Iarlori, Sabrina Moaveninejad, Sadaf Porcaro, Camillo Tecchio, Franca D'Onofrio, Valentina Ferracuti, Francesco Monteriù, Andrea |
Author_xml | – sequence: 1 givenname: Sadaf surname: Moaveninejad fullname: Moaveninejad, Sadaf organization: Department of Neuroscience, University of Padova, 35128 Padua, Italy – sequence: 2 givenname: Valentina surname: D'Onofrio fullname: D'Onofrio, Valentina organization: Padova Neuroscience Center (PNC), University of Padova, 35131 Padua, Italy – sequence: 3 givenname: Franca surname: Tecchio fullname: Tecchio, Franca organization: Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy – sequence: 4 givenname: Francesco surname: Ferracuti fullname: Ferracuti, Francesco organization: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy – sequence: 5 givenname: Sabrina surname: Iarlori fullname: Iarlori, Sabrina organization: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy – sequence: 6 givenname: Andrea surname: Monteriù fullname: Monteriù, Andrea organization: Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy – sequence: 7 givenname: Camillo surname: Porcaro fullname: Porcaro, Camillo email: camillo.porcaro@unipd.it organization: Department of Neuroscience, University of Padova, 35128 Padua, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padua, Italy; Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK. Electronic address: camillo.porcaro@unipd.it |
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Keywords | Brain-computer interface Electroencephalography Motor imagery Voting classifiers Support vector machine Fractal dimension |
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