Continuous Force Decoding from Deep Brain Local Field Potentials for Brain Computer Interfacing.
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ABSTRACT:
SUBMITTER: Shah SA
PROVIDER: S-EPMC5557341 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:The basal ganglia are known to be involved in the planning, execution and control of gripping force and movement vigour. Here we aim to define the nature of the basal ganglia control signal for force and to decode gripping force based on local field potential (LFP) activities recorded from the subthalamic nucleus (STN) in patients with deep brain stimulation (DBS) electrodes. We found that STN LFP activities in the gamma (55-90 Hz) and beta (13-30m Hz) bands were most informative about gripping force, and that a first order dynamic linear model with these STN LFP features as inputs can be used to decode the temporal profile of gripping force. Our results enhance the understanding of how the basal ganglia control gripping force, and also suggest that deep brain LFPs could potentially be used to decode movement parameters related to force and movement vigour for the development of advanced human-machine interfaces.
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Project description:Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.
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Project description:OBJECTIVE:Local field potential (LFP) recordings along a deep brain stimulation (DBS) lead can provide useful feedback for titrating DBS therapy. However, conventional DBS leads with four cylindrical macroelectrodes likely undersample the spatial distribution of sinks and sources in a given brain region. In this study, we investigated the spectral power and spatial feature sizes of LFP activity in non-human primate subthalamic nucleus and globus pallidus using chronically implanted 32-channel directional DBS arrays. APPROACH:Subthalamic nucleus and globus pallidus LFP signals were recorded from directional DBS arrays in the resting state and during a reach-and-retrieval task in two non-human primates in naïve and parkinsonian conditions. LFP recordings were compared amongst bipolar pairs of electrodes using individual and grouped electrode configurations, with the latter mimicking the cylindrical macroelectrode configurations used in current clinical LFP recordings. MAIN RESULTS:Recordings from these DBS arrays showed that (1) beta oscillations have spatial 'fingerprints' in the subthalamic nucleus and globus pallidus, and (2) that these oscillations were muted when grouping electrode contacts together to create cylindrical macroelectrodes similar in relative dimension to those used clinically. Further, these maps depended on parkinsonian condition and whether the subject was resting or performing a motor task. SIGNIFICANCE:Development of future closed-loop DBS therapies that rely on LFP feedback will benefit from implanting DBS arrays with electrode sizes and spacings that are more consistent with the dimensions of oscillatory sinks and sources within the brain.
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Project description:BackgroundAlthough recently introduced directional DBS leads provide control of the stimulation field, programing is time-consuming.ObjectivesHere, we validate local field potentials recorded from directional contacts as a predictor of the most efficient contacts for stimulation in patients with PD.MethodsIntraoperative local field potentials were recorded from directional contacts in the STN of 12 patients and beta activity compared with the results of the clinical contact review performed after 4 to 7 months.ResultsNormalized beta activity was positively correlated with the contact's clinical efficacy. The two contacts with the highest beta activity included the most efficient stimulation contact in up to 92% and that with the widest therapeutic window in 74% of cases.ConclusionLocal field potentials predict the most efficient stimulation contacts and may provide a useful tool to expedite the selection of the optimal contact for directional DBS. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Project description:BackgroundEssential tremor (ET) is one of the most common movement disorders, and continuous deep brain stimulation (DBS) is an established treatment for medication-refractory cases. However, the need for increasing stimulation intensities, with unpleasant side effects, and DBS tolerance over time can be problematic. The advent of novel DBS devices now provides the opportunity to longitudinally record LFPs using the implanted pulse generator, which opens up possibilities to implement adaptive DBS algorithms in a real-life setting.MethodsHere we report a case of thalamic LFP activity recorded using a commercially available sensing-enabled DBS pulse generator (Medtronic Percept PC).ResultsIn the OFF-stimulation condition, a peak tremor frequency of 3.8 Hz was identified during tremor evoking movements as assessed by video and accelerometers. Activity at the same and supraharmonic frequency was seen in the frequency spectrum of the LFP data from the left vim nucleus during motor tasks. Coherence analysis showed that peripherally recorded tremor was coherent with the LFP signal at the tremor frequency and supraharmonic frequency.ConclusionThis is the first report of recorded tremor-related thalamic activity using the electrodes and pulse generator of an implanted DBS system. Larger studies are needed to evaluate the clinical potential of these fully implantable systems, and ultimately pulse generators with sensing-coupled algorithms driving stimulation, to really close the loop.
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Project description:In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations.
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Project description:BackgroundBrain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal.ResultsThe performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ([Formula: see text]) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity.ConclusionThe proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.
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Project description:Background:Directional deep brain stimulation (DBS) technology aims to address the limitations, such as stimulation-induced side effects, by delivering selective, focal modulation via segmented contacts. However, DBS programming becomes more complex and time-consuming for clinical feasibility. Local field potentials (LFPs) might serve a functional role in guiding clinical programming. Objective:In this pilot study, we investigated the spectral dynamics of directional LFPs in subthalamic nucleus (STN) and their relationship to motor symptoms of Parkinson's disease (PD). Methods:We recorded intraoperative STN-LFPs from 8-contact leads (Infinity-6172, Abbott Laboratories, Illinois, United States) in 8 PD patients at rest. Directional LFPs were referenced to their common average and time-frequency analysis was computed using a modified Welch periodogram method. The beta band (13-35 Hz) features were extracted and their correlation to preoperative UPDRS-III scores were assessed. Results:Normalized beta power (13-20 Hz) and normalized peak power (13-35 Hz) were found to be higher in anterior direction despite lack of statistical significance (p > 0.05). Results of the Spearman correlation analysis demonstrated positive trends with bradykinesia/rigidity in dorsoanterior direction (r = 0.659, p = 0.087) and with axial scores in the dorsomedial direction (r = 0.812, p = 0.072). Conclusion:Given that testing all possible combinations of contact pairs and stimulation parameters is not feasible in a single clinic visit, spatio-spectral LFP dynamics obtained from intraoperative recordings might be used as an initial marker to select optimal contact(s).
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Project description:BackgroundHigh-frequency thalamic stimulation is an effective therapy for essential tremor, which mainly affects voluntary movements and/or sustained postures. However, continuous stimulation may deliver unnecessary current to the brain due to the intermittent nature of the tremor.ObjectiveWe proposed to close the loop of thalamic stimulation by detecting tremor-provoking movement states using local field potentials recorded from the same electrodes implanted for stimulation, so that the stimulation is only delivered when necessary.MethodsEight patients with essential tremor participated in this study. Patient-specific support vector machine classifiers were first trained using data recorded while the patient performed tremor-provoking movements. Then, the trained models were applied in real-time to detect these movements and triggered the delivery of stimulation.ResultsUsing the proposed method, stimulation was switched on for 80.37 ± 7.06% of the time when tremor-evoking movements were present. In comparison, the stimulation was switched on for 12.71 ± 7.06% of the time when the patients were at rest and tremor-free. Compared with continuous stimulation, a similar amount of tremor suppression was achieved while only delivering 36.62 ± 13.49% of the energy used in continuous stimulation.ConclusionsThe results suggest that responsive thalamic stimulation for essential tremor based on tremor-provoking movement detection can be achieved without any requirement for external sensors or additional electrocorticography strips. Further research is required to investigate whether the decoding model is stable across time and generalizable to the variety of activities patients may engage with in everyday life. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Project description:Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as 'locked-in syndrome'. Communication in this state is often reduced to selecting individual letters or words by arduous residual movements. More intuitive and rapid communication may be restored by directly interfacing with language areas of the cerebral cortex. We used a grid of closely spaced, nonpenetrating micro-electrodes to record local field potentials (LFPs) from the surface of face motor cortex and Wernicke's area. From these LFPs we were successful in classifying a small set of words on a trial-by-trial basis at levels well above chance. We found that the pattern of electrodes with the highest accuracy changed for each word, which supports the idea that closely spaced micro-electrodes are capable of capturing neural signals from independent neural processing assemblies. These results further support using cortical surface potentials (electrocorticography) in brain-computer interfaces. These results also show that LFPs recorded from the cortical surface (micro-electrocorticography) of language areas can be used to classify speech-related cortical rhythms and potentially restore communication to locked-in patients.
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