Smartphone Household Wireless Electroencephalogram Hat
Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting re...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2013/241489 |
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| _version_ | 1832553343521652736 |
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| author | Harold Szu Charles Hsu Gyu Moon Takeshi Yamakawa Binh Q. Tran Tzyy Ping Jung Joseph Landa |
| author_facet | Harold Szu Charles Hsu Gyu Moon Takeshi Yamakawa Binh Q. Tran Tzyy Ping Jung Joseph Landa |
| author_sort | Harold Szu |
| collection | DOAJ |
| description | Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m≪N≪10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences). |
| format | Article |
| id | doaj-art-8e745da3547c466c88a15aa2dccb7999 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-8e745da3547c466c88a15aa2dccb79992025-02-03T05:54:21ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322013-01-01201310.1155/2013/241489241489Smartphone Household Wireless Electroencephalogram HatHarold Szu0Charles Hsu1Gyu Moon2Takeshi Yamakawa3Binh Q. Tran4Tzyy Ping Jung5Joseph Landa6Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USATrident Systems Inc., Fairfax, VA 22030, USADepartment of Electronic Engineering, Hallym University, Chuncheon, Gangwon-do 200-702, Republic of KoreaFuzzy Logic System Institute, Semiconductor Center, Kitakyushu Science and Research Park, Kitakyushu 808-0135, Fukuoka, JapanDepartment of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USASwartz Center, University of California, San Diego, CA 92093, USABriartek Inc., Alexandria, VA 22301, USARudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m≪N≪10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).http://dx.doi.org/10.1155/2013/241489 |
| spellingShingle | Harold Szu Charles Hsu Gyu Moon Takeshi Yamakawa Binh Q. Tran Tzyy Ping Jung Joseph Landa Smartphone Household Wireless Electroencephalogram Hat Applied Computational Intelligence and Soft Computing |
| title | Smartphone Household Wireless Electroencephalogram Hat |
| title_full | Smartphone Household Wireless Electroencephalogram Hat |
| title_fullStr | Smartphone Household Wireless Electroencephalogram Hat |
| title_full_unstemmed | Smartphone Household Wireless Electroencephalogram Hat |
| title_short | Smartphone Household Wireless Electroencephalogram Hat |
| title_sort | smartphone household wireless electroencephalogram hat |
| url | http://dx.doi.org/10.1155/2013/241489 |
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