Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing
Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be d...
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| Format: | Conference item |
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IEEE
2017
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| _version_ | 1797091328581959680 |
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| author | Shah, S Tan, H Brown, P |
| author_facet | Shah, S Tan, H Brown, P |
| author_sort | Shah, S |
| collection | OXFORD |
| description | Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson’s disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features. The results suggest that high gamma band (300-500Hz) activity, in addition to the beta (13- 30Hz) and gamma band (55-90Hz) activity is the most informative for force prediction but combining all features led to better decoding performance. Furthermore, LFP signals preceding the force output by up to 1256 milliseconds were found to be predictive of the force output. |
| first_indexed | 2024-03-07T03:31:25Z |
| format | Conference item |
| id | oxford-uuid:badb6084-6553-4b15-9911-7d3fe00ef282 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T03:31:25Z |
| publishDate | 2017 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:badb6084-6553-4b15-9911-7d3fe00ef2822022-03-27T05:12:45ZContinuous force decoding from deep brain local field potentials for Brain Computer InterfacingConference itemhttp://purl.org/coar/resource_type/c_5794uuid:badb6084-6553-4b15-9911-7d3fe00ef282Symplectic Elements at OxfordIEEE2017Shah, STan, HBrown, PCurrent Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson’s disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features. The results suggest that high gamma band (300-500Hz) activity, in addition to the beta (13- 30Hz) and gamma band (55-90Hz) activity is the most informative for force prediction but combining all features led to better decoding performance. Furthermore, LFP signals preceding the force output by up to 1256 milliseconds were found to be predictive of the force output. |
| spellingShingle | Shah, S Tan, H Brown, P Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title | Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title_full | Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title_fullStr | Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title_full_unstemmed | Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title_short | Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing |
| title_sort | continuous force decoding from deep brain local field potentials for brain computer interfacing |
| work_keys_str_mv | AT shahs continuousforcedecodingfromdeepbrainlocalfieldpotentialsforbraincomputerinterfacing AT tanh continuousforcedecodingfromdeepbrainlocalfieldpotentialsforbraincomputerinterfacing AT brownp continuousforcedecodingfromdeepbrainlocalfieldpotentialsforbraincomputerinterfacing |