Javier Castillo-Garcia, S. Muller, E. C. Bravo, T. Filho, A. D. Souza
{"title":"基于SSVEP和ERD的非疲劳脑机接口指挥自动驾驶汽车","authors":"Javier Castillo-Garcia, S. Muller, E. C. Bravo, T. Filho, A. D. Souza","doi":"10.1142/S2424922X18400053","DOIUrl":null,"url":null,"abstract":"This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"10 1","pages":"1840005:1-1840005:22"},"PeriodicalIF":0.9000,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Nonfatigating Brain-Computer Interface Based on SSVEP and ERD to Command an Autonomous Car\",\"authors\":\"Javier Castillo-Garcia, S. Muller, E. C. Bravo, T. Filho, A. D. Souza\",\"doi\":\"10.1142/S2424922X18400053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.\",\"PeriodicalId\":47145,\"journal\":{\"name\":\"Advances in Data Science and Adaptive Analysis\",\"volume\":\"10 1\",\"pages\":\"1840005:1-1840005:22\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2018-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Data Science and Adaptive Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S2424922X18400053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Data Science and Adaptive Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S2424922X18400053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Nonfatigating Brain-Computer Interface Based on SSVEP and ERD to Command an Autonomous Car
This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.