Review of the State-of-the-Art of Brain-Controlled Vehicles
Brain-Controlled Vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on brain-controlled vehicles, with a special reference to the terrestrial BCV (e.g., the mobile car, car simulator, real car, graphical and gami...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9499083/ |
| _version_ | 1818645812159184896 |
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| author | Amin Hekmatmanesh Pedro H. J. Nardelli Heikki Handroos |
| author_facet | Amin Hekmatmanesh Pedro H. J. Nardelli Heikki Handroos |
| author_sort | Amin Hekmatmanesh |
| collection | DOAJ |
| description | Brain-Controlled Vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on brain-controlled vehicles, with a special reference to the terrestrial BCV (e.g., the mobile car, car simulator, real car, graphical and gaming car) and the aerial BCV, also called BCAV (e.g., real quadcopters, drones, fixed wings, graphical helicopter, and aircraft) controlled by using bio-signals, such as electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). For instance, EEG-based algorithms detect patterns from the motor imaginary cortex area of the brain for intention detection, patterns like event-related desynchronization/event-related synchronization, steady-state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best-performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization, and chaotic tug of war. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with statistical analysis. The present work provides an extensive literature review of the key findings of the past ten years, indicating future perspectives in the field. |
| first_indexed | 2024-12-17T00:36:41Z |
| format | Article |
| id | doaj.art-25a088c84a954debabb482f7036eb8b9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:36:41Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25a088c84a954debabb482f7036eb8b92022-12-21T22:10:08ZengIEEEIEEE Access2169-35362021-01-01911017311019310.1109/ACCESS.2021.31007009499083Review of the State-of-the-Art of Brain-Controlled VehiclesAmin Hekmatmanesh0https://orcid.org/0000-0002-6117-4683Pedro H. J. Nardelli1https://orcid.org/0000-0002-7398-1802Heikki Handroos2https://orcid.org/0000-0002-9479-0968Laboratory of Intelligent Machines, LUT University, Lappeenranta, FinlandSchool of Energy Systems, LUT University, Lappeenranta, FinlandLaboratory of Intelligent Machines, LUT University, Lappeenranta, FinlandBrain-Controlled Vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on brain-controlled vehicles, with a special reference to the terrestrial BCV (e.g., the mobile car, car simulator, real car, graphical and gaming car) and the aerial BCV, also called BCAV (e.g., real quadcopters, drones, fixed wings, graphical helicopter, and aircraft) controlled by using bio-signals, such as electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). For instance, EEG-based algorithms detect patterns from the motor imaginary cortex area of the brain for intention detection, patterns like event-related desynchronization/event-related synchronization, steady-state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best-performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization, and chaotic tug of war. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with statistical analysis. The present work provides an extensive literature review of the key findings of the past ten years, indicating future perspectives in the field.https://ieeexplore.ieee.org/document/9499083/Bio-signal patternscontrolmachine learningartificial intelligence simulatorvehicleaerial vehicle |
| spellingShingle | Amin Hekmatmanesh Pedro H. J. Nardelli Heikki Handroos Review of the State-of-the-Art of Brain-Controlled Vehicles IEEE Access Bio-signal patterns control machine learning artificial intelligence simulator vehicle aerial vehicle |
| title | Review of the State-of-the-Art of Brain-Controlled Vehicles |
| title_full | Review of the State-of-the-Art of Brain-Controlled Vehicles |
| title_fullStr | Review of the State-of-the-Art of Brain-Controlled Vehicles |
| title_full_unstemmed | Review of the State-of-the-Art of Brain-Controlled Vehicles |
| title_short | Review of the State-of-the-Art of Brain-Controlled Vehicles |
| title_sort | review of the state of the art of brain controlled vehicles |
| topic | Bio-signal patterns control machine learning artificial intelligence simulator vehicle aerial vehicle |
| url | https://ieeexplore.ieee.org/document/9499083/ |
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