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Conference Paper: A new approach for single-trial detection of laser-evoked potentials and its application to pain prediction
Title | A new approach for single-trial detection of laser-evoked potentials and its application to pain prediction |
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Authors | |
Issue Date | 2013 |
Citation | The 9th International Conference on Information, Communications and Signal Processing (ICICS 2013), Tainan, Taiwan, 10-13 December 2013. How to Cite? |
Abstract | Single-trial detection of evoked brain potentials is essential for many research topics in neural engineering and neuroscience. In present study, a novel approach, which combines common spatial pattern (CSP) and multiple linear regression (MLR), is proposed to for single-trial detection of pain-related laser-evoked potentials (LEPs). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR makes an automatic and reliable estimation of the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The MLR coefficients are further used for the prediction of pain perception, which is of great importance for both basic and clinical applications. The prediction is performed with both binary (classification of low pain and high pain) and continuous (regression on a continuous scale from 0 to 10) outcomes. The results show that the proposed methods could provide reliable performance at both with- and cross-individual levels. |
Description | Session Fr11 Signal Processing for Biomedical Applications - Fr11.3 A New Approach for Single-trial Detection of Laser-evoked Potentials and its Application to Pain Prediction: no. Fr11.3 - P0379 |
Persistent Identifier | http://hdl.handle.net/10722/189882 |
DC Field | Value | Language |
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dc.contributor.author | Huang, G | en_US |
dc.contributor.author | Xiao, P | en_US |
dc.contributor.author | Hu, L | en_US |
dc.contributor.author | Hung, YS | en_US |
dc.contributor.author | Zhang, Z | en_US |
dc.date.accessioned | 2013-09-17T15:01:05Z | - |
dc.date.available | 2013-09-17T15:01:05Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 9th International Conference on Information, Communications and Signal Processing (ICICS 2013), Tainan, Taiwan, 10-13 December 2013. | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/189882 | - |
dc.description | Session Fr11 Signal Processing for Biomedical Applications - Fr11.3 A New Approach for Single-trial Detection of Laser-evoked Potentials and its Application to Pain Prediction: no. Fr11.3 - P0379 | - |
dc.description.abstract | Single-trial detection of evoked brain potentials is essential for many research topics in neural engineering and neuroscience. In present study, a novel approach, which combines common spatial pattern (CSP) and multiple linear regression (MLR), is proposed to for single-trial detection of pain-related laser-evoked potentials (LEPs). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR makes an automatic and reliable estimation of the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The MLR coefficients are further used for the prediction of pain perception, which is of great importance for both basic and clinical applications. The prediction is performed with both binary (classification of low pain and high pain) and continuous (regression on a continuous scale from 0 to 10) outcomes. The results show that the proposed methods could provide reliable performance at both with- and cross-individual levels. | - |
dc.language | eng | en_US |
dc.relation.ispartof | 9th ICICS 2013 | en_US |
dc.title | A new approach for single-trial detection of laser-evoked potentials and its application to pain prediction | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Huang, G: huanggan@hku.hk | en_US |
dc.identifier.email | Hung, YS: yshung@eee.hku.hk | en_US |
dc.identifier.email | Zhang, Z: zgzhang@eee.hku.hk | en_US |
dc.identifier.authority | Hung, YS=rp00220 | en_US |
dc.identifier.authority | Zhang, Z=rp01565 | en_US |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.hkuros | 223286 | en_US |