Classification of Relaxation and Concentration Mental States with EEG
In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obta...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Online Access: | https://www.mdpi.com/2078-2489/12/5/187 |
| _version_ | 1797536249897025536 |
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| author | Shingchern D. You |
| author_facet | Shingchern D. You |
| author_sort | Shingchern D. You |
| collection | DOAJ |
| description | In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula> bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi><mo>+</mo><mi>β</mi><mo>+</mo><mi>γ</mi></mrow></semantics></math></inline-formula> bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device. |
| first_indexed | 2024-03-10T11:57:53Z |
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| language | English |
| last_indexed | 2024-03-10T11:57:53Z |
| publishDate | 2021-04-01 |
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| spelling | doaj.art-5bc9254882344f569b85b583ffad24e52023-11-21T17:10:47ZengMDPI AGInformation2078-24892021-04-0112518710.3390/info12050187Classification of Relaxation and Concentration Mental States with EEGShingchern D. You0Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, TaiwanIn this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula> bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi><mo>+</mo><mi>β</mi><mo>+</mo><mi>γ</mi></mrow></semantics></math></inline-formula> bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.https://www.mdpi.com/2078-2489/12/5/187EEGmental statesSVMneural networks |
| spellingShingle | Shingchern D. You Classification of Relaxation and Concentration Mental States with EEG Information EEG mental states SVM neural networks |
| title | Classification of Relaxation and Concentration Mental States with EEG |
| title_full | Classification of Relaxation and Concentration Mental States with EEG |
| title_fullStr | Classification of Relaxation and Concentration Mental States with EEG |
| title_full_unstemmed | Classification of Relaxation and Concentration Mental States with EEG |
| title_short | Classification of Relaxation and Concentration Mental States with EEG |
| title_sort | classification of relaxation and concentration mental states with eeg |
| topic | EEG mental states SVM neural networks |
| url | https://www.mdpi.com/2078-2489/12/5/187 |
| work_keys_str_mv | AT shingcherndyou classificationofrelaxationandconcentrationmentalstateswitheeg |