{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1052"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1052","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"A comparative analysis of haptic and EEG devices for evaluation and training of post-stroke patients within a virtual environment","abstract":"Virtual Rehabilitation benefits from the usage of interfaces other than the mouse and keyboard, but also possess disadvantages: haptic peripherals can utilize the subject's hand to provide position information or joint angles, and allow direct training for specific movements; but can also place unneeded strain on the limbs; brain-machine interfaces (BMI) can provide direct connections from the user to external hardware or software, but are currently inaccurate for the full diversity of user movements in daily life and require invasive surgery to implement. A compromise between these two extremes is a BMI that can be adapted to specific users, can function with a wide range of hardware and software, and is both noninvasive and convenient to wear for extended periods of time. A suitable BMI using Electroencephalography (EEG) input, known as the Emotiv EPOC^{TM} by Emotiv Systems was evaluated using multiple input specializations and tested with an external robotic arm to determine if it was suitable for control of peripherals. Users were given a preset periodicity to follow in order to evaluate their ability to translate specific facial movements into commands as well as their responsiveness to change the robot arm's direction. Within 2 weeks of training, they maintained or improved axial control of the robot arm, and reduced their overall performance time. Although the EPOC^{TM} does require further testing and development, its adaptability to multiple software programs, users and peripherals allows it to serve both Virtual Rehabilitation and device control in the immediate future.","abstract_html":"Virtual Rehabilitation benefits from the usage of interfaces other than the mouse and keyboard, but also possess disadvantages: haptic peripherals can utilize the subject's hand to provide position information or joint angles, and allow direct training for specific movements; but can also place unneeded strain on the limbs; brain-machine interfaces (BMI) can provide direct connections from the user to external hardware or software, but are currently inaccurate for the full diversity of user movements in daily life and require invasive surgery to implement. A compromise between these two extremes is a BMI that can be adapted to specific users, can function with a wide range of hardware and software, and is both noninvasive and convenient to wear for extended periods of time. A suitable BMI using Electroencephalography (EEG) input, known as the Emotiv EPOC^{TM} by Emotiv Systems was evaluated using multiple input specializations and tested with an external robotic arm to determine if it was suitable for control of peripherals. Users were given a preset periodicity to follow in order to evaluate their ability to translate specific facial movements into commands as well as their responsiveness to change the robot arm's direction. Within 2 weeks of training, they maintained or improved axial control of the robot arm, and reduced their overall performance time. Although the EPOC^{TM} does require further testing and development, its adaptability to multiple software programs, users and peripherals allows it to serve both Virtual Rehabilitation and device control in the immediate future.","abstract_has_math":false,"creators":["Ranky, Gregory Nicholas"],"institution":null,"degree_name":"Master of Science in Biomedical Engineering - (M.S.)","degree_level":null,"degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Sergei Adamovich","Richard A. Foulds","Mesut Sahin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-31T08:00:00Z","date_published":"2010-01-31T08:00:00Z","updated_at":"2026-07-24T03:22:19Z","subjects":["Brain-machine interfaces","Haptic peripherals","Biomedical Engineering and Bioengineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/53","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sergei Adamovich","Richard A. 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A compromise between these two extremes is a BMI that can be adapted to specific users, can function with a wide range of hardware and software, and is both noninvasive and convenient to wear for extended periods of time. A suitable BMI using Electroencephalography (EEG) input, known as the Emotiv EPOC^{TM} by Emotiv Systems was evaluated using multiple input specializations and tested with an external robotic arm to determine if it was suitable for control of peripherals. Users were given a preset periodicity to follow in order to evaluate their ability to translate specific facial movements into commands as well as their responsiveness to change the robot arm's direction. Within 2 weeks of training, they maintained or improved axial control of the robot arm, and reduced their overall performance time. Although the EPOC^{TM} does require further testing and development, its adaptability to multiple software programs, users and peripherals allows it to serve both Virtual Rehabilitation and device control in the immediate future."]},{"key":"dc:title","label":"Title","values":["A comparative analysis of haptic and EEG devices for evaluation and training of post-stroke patients within a virtual environment"]}]}],"canonical_facts":{"dc:contributor":["Sergei Adamovich","Richard A. Foulds","Mesut Sahin"],"dc:creator":["Ranky, Gregory Nicholas"],"dc:description.abstract":["Virtual Rehabilitation benefits from the usage of interfaces other than the mouse and keyboard, but also possess disadvantages: haptic peripherals can utilize the subject's hand to provide position information or joint angles, and allow direct training for specific movements; but can also place unneeded strain on the limbs; brain-machine interfaces (BMI) can provide direct connections from the user to external hardware or software, but are currently inaccurate for the full diversity of user movements in daily life and require invasive surgery to implement. A compromise between these two extremes is a BMI that can be adapted to specific users, can function with a wide range of hardware and software, and is both noninvasive and convenient to wear for extended periods of time. A suitable BMI using Electroencephalography (EEG) input, known as the Emotiv EPOC^{TM} by Emotiv Systems was evaluated using multiple input specializations and tested with an external robotic arm to determine if it was suitable for control of peripherals. Users were given a preset periodicity to follow in order to evaluate their ability to translate specific facial movements into commands as well as their responsiveness to change the robot arm's direction. Within 2 weeks of training, they maintained or improved axial control of the robot arm, and reduced their overall performance time. Although the EPOC^{TM} does require further testing and development, its adaptability to multiple software programs, users and peripherals allows it to serve both Virtual Rehabilitation and device control in the immediate future."],"dc:identifier":["https://digitalcommons.njit.edu/theses/53"],"dc:subject":["Brain-machine interfaces","Haptic peripherals","Biomedical Engineering and Bioengineering"],"dc:title":["A comparative analysis of haptic and EEG devices for evaluation and training of post-stroke patients within a virtual environment"],"dc:type":["Thesis"],"thesis:degree_discipline":["Biomedical Engineering"],"thesis:degree_name":["Master of Science in Biomedical Engineering - (M.S.)"]},"updated_at":"2026-07-24T03:22:19Z"}