Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors
During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/13/8/10273 |
| _version_ | 1798024560480616448 |
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| author | Hsuan-Chin Chu Ning-Han Liu Cheng-Yu Chiang |
| author_facet | Hsuan-Chin Chu Ning-Han Liu Cheng-Yu Chiang |
| author_sort | Hsuan-Chin Chu |
| collection | DOAJ |
| description | During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students’ expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects’ EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future. |
| first_indexed | 2024-04-11T18:04:38Z |
| format | Article |
| id | doaj.art-d03abd8f1fc3437a9f7c4ddf4ab17775 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:04:38Z |
| publishDate | 2013-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d03abd8f1fc3437a9f7c4ddf4ab177752022-12-22T04:10:22ZengMDPI AGSensors1424-82202013-08-01138102731028610.3390/s130810273Recognizing the Degree of Human Attention Using EEG Signals from Mobile SensorsHsuan-Chin ChuNing-Han LiuCheng-Yu ChiangDuring the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students’ expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects’ EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.http://www.mdpi.com/1424-8220/13/8/10273electroencephalogramattention statuselectroencephalography (EEG) classificationsupport vector machine |
| spellingShingle | Hsuan-Chin Chu Ning-Han Liu Cheng-Yu Chiang Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors Sensors electroencephalogram attention status electroencephalography (EEG) classification support vector machine |
| title | Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors |
| title_full | Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors |
| title_fullStr | Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors |
| title_full_unstemmed | Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors |
| title_short | Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors |
| title_sort | recognizing the degree of human attention using eeg signals from mobile sensors |
| topic | electroencephalogram attention status electroencephalography (EEG) classification support vector machine |
| url | http://www.mdpi.com/1424-8220/13/8/10273 |
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