{"title":"电动轮椅脑控驾驶辅助装置","authors":"Nikhil Shinde, K. George","doi":"10.1109/BSN.2016.7516243","DOIUrl":null,"url":null,"abstract":"The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Brain-controlled driving aid for electric wheelchairs\",\"authors\":\"Nikhil Shinde, K. George\",\"doi\":\"10.1109/BSN.2016.7516243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.\",\"PeriodicalId\":205735,\"journal\":{\"name\":\"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BSN.2016.7516243\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2016.7516243","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Brain-controlled driving aid for electric wheelchairs
The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.