Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs
Brain–Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal qualit...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-11-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.01248/full |
| _version_ | 1818836969975709696 |
|---|---|
| author | Carlos A. Loza Carlos A. Loza Chandan G. Reddy Chandan G. Reddy Chandan G. Reddy Shailaja Akella José C. Príncipe |
| author_facet | Carlos A. Loza Carlos A. Loza Chandan G. Reddy Chandan G. Reddy Chandan G. Reddy Shailaja Akella José C. Príncipe |
| author_sort | Carlos A. Loza |
| collection | DOAJ |
| description | Brain–Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm–specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions—a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85–145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain. |
| first_indexed | 2024-12-19T03:15:03Z |
| format | Article |
| id | doaj.art-d8bef48f0fc3452d952b7a20082db5f2 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-19T03:15:03Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-d8bef48f0fc3452d952b7a20082db5f22022-12-21T20:37:54ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-11-011310.3389/fnins.2019.01248467574Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGsCarlos A. Loza0Carlos A. Loza1Chandan G. Reddy2Chandan G. Reddy3Chandan G. Reddy4Shailaja Akella5José C. Príncipe6Department of Mathematics, Universidad San Francisco de Quito, Quito, EcuadorInstituto de Neurociencias, Universidad San Francisco de Quito, Quito, EcuadorDepartment of Neurosurgery, University of Iowa, Iowa City, IA, United StatesDepartment of Neurosurgery, University of Florida, Gainesville, FL, United StatesComputational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United StatesComputational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United StatesComputational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United StatesBrain–Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm–specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions—a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85–145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain.https://www.frontiersin.org/article/10.3389/fnins.2019.01248/fullbrain-computer interfaceselectrocoticogram (ECoG)generative modelminimum description length (MDL)representation learningtemporal marked point process |
| spellingShingle | Carlos A. Loza Carlos A. Loza Chandan G. Reddy Chandan G. Reddy Chandan G. Reddy Shailaja Akella José C. Príncipe Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs Frontiers in Neuroscience brain-computer interfaces electrocoticogram (ECoG) generative model minimum description length (MDL) representation learning temporal marked point process |
| title | Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs |
| title_full | Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs |
| title_fullStr | Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs |
| title_full_unstemmed | Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs |
| title_short | Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs |
| title_sort | discrimination of movement related cortical potentials exploiting unsupervised learned representations from ecogs |
| topic | brain-computer interfaces electrocoticogram (ECoG) generative model minimum description length (MDL) representation learning temporal marked point process |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.01248/full |
| work_keys_str_mv | AT carlosaloza discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT carlosaloza discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT chandangreddy discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT chandangreddy discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT chandangreddy discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT shailajaakella discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs AT josecprincipe discriminationofmovementrelatedcorticalpotentialsexploitingunsupervisedlearnedrepresentationsfromecogs |