A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal

Golshan, Hosein M.; Hebb, Adam O.; Hanrahan, Sara J.; Nedrud, Joshua; Mahoor, Mohammad H.

doi:10.1109/embc.2016.7590878

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Fitting the SVM-PCA classifier was more computationally demanding, given the temporal longitudinal aspect of the dataset. Adjusting f o could help in this regard ( Golshan et al, 2016 , 2018 , 2020 ). In addition, the SVM algorithm does not yield prediction probabilities.…”

Section: Discussionmentioning

confidence: 99%

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Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Martineau

Vaidyanathan

et al. 2023

Front. Hum. Neurosci.

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IntroductionDecoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience.MethodsThis study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson’s disease patients.ResultsDetection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models.DiscussionHyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study’s findings can inform future design iterations of neural decoders for adaptive DBS and BCI.

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Section: Discussionmentioning

confidence: 99%

“…Its setting acted in part as a form of feature selector. (4) Thanks to their versatile non-linear kernels, support vector machine (SVM) classifiers have been regularly employed for high-performance decoding ( Mamun et al, 2015 ; Golshan et al, 2016 , 2018 ; Nandy et al, 2019 ; He et al, 2021 ). The radial-basis variant was chosen.…”

Section: Methodsmentioning

confidence: 99%

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Martineau

Vaidyanathan

et al. 2023

Front. Hum. Neurosci.

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“…Classifying human behavior from brain signals has also been explored in developing closed loop aDBS [75][76][77][78]. The real-time classification of human behaviors would allow for adaptively optimizing stimulation parameters per task.…”

Section: Adaptive Dbsmentioning

confidence: 99%

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

et al. 2020

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Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

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“…Nevertheless so far, more advanced evaluation techniques such as machine learning based pattern recognition, especially using deep neural networks, has been applied only rarely to detect and characterize single trial LFP events. Thus, more coarse-grained features such as spectral cues have been analyzed using machine learning [25]. Nurse and co-workers used a top-down approach to classify LFP data by training a convolutional neural network on raw data [26].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based decoding of local field potential events

Schilling

Gerum

Boehm

et al. 2022

Preprint

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How is information processed in the cerebral cortex? To answer this question a lot of effort has been undertaken to create novel and to further develop existing neuroimaging techniques. Thus, a high spatial resolution of fMRI devices was the key to exactly localize cognitive processes. Furthermore, an increase in time-resolution and number of recording channels of electro-physiological setups has opened the door to investigate the exact timing of neural activity. However, in most cases the recorded signal is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. Here, we show that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial base. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both neural extra-cellular recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

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A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal

Cited by 7 publications

References 18 publications

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

Deep learning based decoding of local field potential events

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