In this thesis, a machine learning based method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG trials were extracted during golfers' preparation of putting. Second, different features are calculated such as correlation coefficient, power spectrum density and coherence, which are used as features for the classification algorithm. To predict golfers' performance, the support vector machine algorithm is used to classify the EEG patterns into two categories corresponding to successful and non-successful putts. The proposed approach utilizes a large number of features extracted from the EEG signals, and it is capable of providing adequate prediction that could help golfers to improve their performances.
","abstract_html":"<p>In this thesis, a machine learning based method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG trials were extracted during golfers' preparation of putting. Second, different features are calculated such as correlation coefficient, power spectrum density and coherence, which are used as features for the classification algorithm. To predict golfers' performance, the support vector machine algorithm is used to classify the EEG patterns into two categories corresponding to successful and non-successful putts. The proposed approach utilizes a large number of features extracted from the EEG signals, and it is capable of providing adequate prediction that could help golfers to improve their performances. </p>","abstract_has_math":false,"creators":["Guo, Qing"],"institution":null,"degree_name":"Master of Science in Electrical Engineering (MSEE)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Li, Baohua","Zhang, Shengfan"],"advisors":["Wu, Jingxian"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-08-01T07:00:00Z","date_published":"2014-08-01T07:00:00Z","updated_at":"2026-07-24T00:58:53Z","subjects":["Coherence","EEG","Golf","SVM","Bioelectrical and Neuroengineering","Biomedical"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/2166","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Baohua","Zhang, Shengfan"]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Wu, Jingxian"]},{"key":"dc:creator","label":"Author","values":["Guo, Qing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-02-06T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Electrical Engineering (MSEE)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Coherence","EEG","Golf","SVM","Bioelectrical and Neuroengineering","Biomedical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/2166"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, a machine learning based method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG trials were extracted during golfers' preparation of putting. Second, different features are calculated such as correlation coefficient, power spectrum density and coherence, which are used as features for the classification algorithm. To predict golfers' performance, the support vector machine algorithm is used to classify the EEG patterns into two categories corresponding to successful and non-successful putts. The proposed approach utilizes a large number of features extracted from the EEG signals, and it is capable of providing adequate prediction that could help golfers to improve their performances.
"]},{"key":"dc:title","label":"Title","values":["Improving Golf Putt Performance with Statistical Learning of EEG Signals"]}]}],"canonical_facts":{"dc:contributor":["Li, Baohua","Zhang, Shengfan"],"dc:contributor.advisor":["Wu, Jingxian"],"dc:creator":["Guo, Qing"],"dc:date":["2014"],"dc:date.available":["2024-02-06T08:00:00Z"],"dc:description.abstract":["In this thesis, a machine learning based method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG trials were extracted during golfers' preparation of putting. Second, different features are calculated such as correlation coefficient, power spectrum density and coherence, which are used as features for the classification algorithm. To predict golfers' performance, the support vector machine algorithm is used to classify the EEG patterns into two categories corresponding to successful and non-successful putts. The proposed approach utilizes a large number of features extracted from the EEG signals, and it is capable of providing adequate prediction that could help golfers to improve their performances.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/2166"],"dc:subject":["Coherence","EEG","Golf","SVM","Bioelectrical and Neuroengineering","Biomedical"],"dc:title":["Improving Golf Putt Performance with Statistical Learning of EEG Signals"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Electrical Engineering (MSEE)"]},"updated_at":"2026-07-24T00:58:53Z"}