Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
| Autor: | Javier Fernández, Yu-Kai Wang, Chin-Teng Lin, Javier Fumanal-Idocin, José Antonio Sanz, Humberto Bustince |
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| Přispěvatelé: | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities, Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas, Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science Interface (computing) Computer Science - Human-Computer Interaction 02 engineering and technology Electroencephalography 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Preprocessor medicine.diagnostic_test Brain Signal Processing Computer-Assisted Human brain Classification Computer Science Applications medicine.anatomical_structure Brain-Computer Interfaces Pattern recognition (psychology) Imagination 020201 artificial intelligence & image processing Algorithms Information Systems Signal processing Computer Science - Artificial Intelligence Feature extraction Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Human-Computer Interaction (cs.HC) Motor imagery medicine FOS: Electrical engineering electronic engineering information engineering Humans Brain-computer-interface (BCI) Electrical and Electronic Engineering Aggregation functions Brain–computer interface business.industry Pattern recognition Human-Computer Interaction Artificial Intelligence (cs.AI) Sensorimotor rhythm Control and Systems Engineering Artificial intelligence Information fusion business Motor imagery (MI) Software |
| Zdroj: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname |
| Popis: | Brain Computer Interface technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery. In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making. In this paper, we define a BCI Framework, named Enhanced Fusion Framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. Firstly, we include aan additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant. Secondly, we add an additional frequency band as feature for the system and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing motor imagery-based brain-computer interface experiments. On this dataset, the new system achieved a 88.80% of accuracy. We also propose an optimized version of our system that is able to obtain up to 90,76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results. IEEE Transactions on Cybernetics (2021) |
| Databáze: | OpenAIRE |
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