Brain Electrodes Research Articles

Design of a Novel Macro-Micro Integrated Brain Surgery Robot Based on Modular Parallel Mechanisms

The advancement and development of medical surgical robots have provided new technological support for brain surgery and neurosurgical procedures, improving the reliability of highly complex and precise surgeries. In turn, this urges the design and development of novel surgical robots to possess higher precision, stability, and enhanced motion capabilities. In response to this practical demand, this paper introduces a macro-micro integrated medical brain surgery robot system based on the concept of modular PMs (parallel mechanisms), which have a total of 13 active DOFs (degrees of freedom). This system divides the motion process of brain surgery into a large-scale macro-motion space and a small-scale high-precision motion space for design and planning control. The introduction of the design concept that combines multiple modular parallel sub-mechanisms has brought a significant level of decoupling characteristics to the mechanism itself. A comprehensive introduction and analysis of the surgical robot are provided, covering aspects such as design, kinematics, motion planning, and performance indicators. To address the pose allocation and coordination of motion between the macro platform and the micro platform, a pose allocation algorithm based on the decoupling and non-decoupling characteristics in various dimensions of the macro-micro platform is proposed. The designed measurement experiments have demonstrated that the repeatability in positioning accuracy of the macro and micro platform reaches the level of micron and submicron respectively. Practical experiments of motion control and simulated brain electrode implantation validate the excellent performance and stability of the entire surgical robot system. This research contributes innovative insights to the development of medical surgical robot systems, particularly in the domain of mechanism design.

EEG emotion recognition based on the TimesNet fusion model

In recent years, emotion recognition based on electroencephalogram (EEG) has become an important research field. This paper proposes an innovative multi-scale emotion recognition method (MS-ERM), which is based on a deep learning model. First, we divide the EEG signal into time windows of 0.5 s in different frequency bands to extract the differential entropy feature and embed the feature into the brain electrode map to express spatial information. Then, the features of each segment are used as input to the new deep learning model (MS-TimesNet). The model combines multi-scale convolution and TimesNet network to effectively extract dynamic time features, cross-channel spatial features, and complex time features in 2D space. Through extensive tests on the DEAP dataset, we prove that this method is superior to existing methods in terms of sentiment classification performance. In the arousal and valence classification, the average classification accuracy of subject-dependent tests reached 91.31% and 90.45%, respectively, while in subject-independent tests, the average classification accuracy was 86.66% and 85.40%, respectively. Code is available at this repository: https://github.com/hyao0827/MS-ERM.

A profile on the WISE cortical strip for intraoperative neurophysiological monitoring

ABSTRACT Introduction During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality. Areas covered We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data. Expert opinion The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.

Haemodynamics of stent-mounted neural interfaces in tapered and deformed blood vessels

The endovascular neural interface provides an appealing minimally invasive alternative to invasive brain electrodes for recording and stimulation. However, stents placed in blood vessels have long been known to affect blood flow (haemodynamics) and lead to neointimal growth within the blood vessel. Both the stent elements (struts and electrodes) and blood vessel wall geometries can affect the mechanical environment on the blood vessel wall, which could lead to unfavourable vascular remodelling after stent placement. With increasing applications of stents and stent-like neural interfaces in venous blood vessels in the brain, it is necessary to understand how stents affect blood flow and tissue growth in veins. We explored the haemodynamics of a stent-mounted neural interface in a blood vessel model. Results indicated that blood vessel deformation and tapering caused a substantial change to the lumen geometry and the haemodynamics. The neointimal proliferation was evaluated in sheep implanted with an endovascular neural interface. Analysis showed a negative correlation with the mean Wall Shear Stress pattern. The results presented here indicate that the optimal stent oversizing ratio must be considered to minimise the haemodynamic impact of stenting.

Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning.

Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS). We propose a DRL approach based on deep Q-learning where the states represent the electrode trajectory and associated information, and actions are the possible motions. Deep neural networks allow to navigate the complex state space derived from MRI data. The chosen reward function emphasizes safety and accuracy in reaching the target structure. The results were compared with a reference (segmented electrode) and a conventional technique. The DRL approach excelled in navigating the complex anatomy, consistently providing safer and more precise electrode placements than the reference. Compared to conventional techniques, it showed an improvement in accuracy of 2.3% in average proximity to obstacles and 19.4% in average orientation angle. Expectedly, computation times rose significantly, from 2 to 18min. Our investigation into DRL for DBS electrode trajectory planning has showcased its promising potential. Despite only delivering modest accuracy gains compared to traditional methods in the single-electrode case, its relevance for problems with high-dimensional state and action spaces and its resilience against local optima highlight its promising role for complex scenarios. This preliminary study constitutes a first step toward the more challenging problem of multiple-electrodes planning.

Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.

In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient’s brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation

Advancements in brain–machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT’s biocompatibility and chemical inertness, the electrode’s flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

Brain abscess – A rare confounding factor for diagnosis of post-traumatic epilepsy after lateral fluid-percussion injury

ObjectiveTo assess the prevalence of brain abscesses as a confounding factor for the diagnosis of post-traumatic epilepsy (PTE) in a rat model of lateral fluid-percussion-induced (FPI) traumatic brain injury (TBI). MethodsThis retrospective study included 583 rats from 3 study cohorts collected over 2009–2022 in a single laboratory. The rats had undergone sham-operation or TBI using lateral FPI. Rats were implanted with epidural and/or intracerebral electrodes for electroencephalogram recordings. Brains were processed for histology to screen for abscess(es). In abscess cases, (a) unfolded cortical maps were constructed to assess the cortical location and area of the abscess, (b) the abscess tissue was Gram stained to determine the presence of gram-positive and gram-negative bacteria, and (c) immunostaining was performed to detect infiltrating neutrophils, T-lymphocytes, and glial cells as tissue biomarkers of inflammation. In vivo and/or ex vivo magnetic resonance images available from a subcohort of animals were reviewed to evaluate the presence of abscesses. Plasma samples available from a subcohort of rats were used for enzyme-linked immunosorbent assays to determine the levels of lipopolysaccharide (LPS) as a circulating biomarker for gram-negative bacteria. ResultsBrain abscesses were detected in 2.6% (15/583) of the rats (6 sham, 9 TBI). In histology, brain abscesses were characterized as vascularized encapsulated lesions filled with neutrophils and surrounded by microglia/macrophages and astrocytes. The abscesses were mainly located under the screw electrodes, support screws, or craniectomy. Epilepsy was diagnosed in 60% (9/15) of rats with an abscess (4 sham, 5 TBI). Of these, 67% (6/9) had seizure clusters. The average seizure frequency in abscess cases was 0.436 ± 0.281 seizures/d. Plasma LPS levels were comparable between rats with and without abscesses (p > 0.05). SignificanceAlthough rare, a brain abscess is a potential confounding factor for epilepsy diagnosis in animal models of structural epilepsies following brain surgery and electrode implantation, particularly if seizures occur in sham-operated experimental controls and/or in clusters.

Movement disorder Deep brain stimulation Hybridization: Patient and caregiver outcomes

Background and ObjectivesDeep brain stimulation (DBS) is a well-established surgical treatment for certain movement disorders and involves the implantation of brain electrodes connected to implantable pulse generators (IPGs). As more device manufacturers have entered the market, some IPG technology has been designed to be compatible with brain electrodes from other manufacturers, which has facilitated the hybridization of implant technology. The aim of this study was to assess the benefits of hybridization of non-rechargeable, constant voltage IPGs to rechargeable, constant current IPGs. MethodsA list of DBS movement disorder patients who had their non-rechargeable, constant voltage IPGs replaced with rechargeable, constant current IPGs from a different manufacturer was compiled. Structured surveys of these patients, and their caregivers when applicable, were undertaken to determine both patient and caregiver satisfaction in this DBS hybridization strategy. ResultsEighteen patients met inclusion criteria and twelve patients or their caregivers completed the structured survey (67% response rate). Nine patients had Parkinson’s disease (75%), three had essential tremor (25%). Nine (75%) were converted from bilateral single-channel IPGs, and three (25%) were converted from a unilateral dual-channel IPGs. Overall, 92% of patients and caregivers surveyed reported improvement or no change in their symptoms, 92% reported a decrease or no change in their medication requirements, and 92% report they are satisfied or very satisfied with their IPG hybridization and would recommend the surgery to similar patients. There were no immediate surgical complications. ConclusionIn this series of movement disorder DBS patients, surgery was safe and patient and caregiver satisfaction were high with a hybridization of non-rechargeable, constant voltage IPGs to rechargeable, constant current IPGs.

A Review on Biomaterials for Neural Interfaces: Enhancing Brain-Machine Interfaces

Biomaterials are essential to the development of neural interfaces, including brainmachine interfaces. Biomaterial methods improve neural interface functionality, compatibility, and longevity, enabling brain-device communication. An extensive investigation of biomaterials utilized in brain electrode arrays, neural probes, & implantable devices rely on how materials affect neural signals recording, stimulation, & tissue contact. It also investigates how biomaterials, bioelectronics and 3D printing could improve neural interfaces. Biomaterials modulate neuroinflammatory responses, enhance brain tissue regeneration, and promote neural interface longevity. This study shows the potential for change of biomaterial-based neural interfaces in neuroprosthetics, neurological rehabilitation, and fundamental neuroscience research, addressing the need for brain-machine relationship and neurotechnology innovation. These findings suggest expanding biomaterials research and development to advance and sustain neural interface technologies for future use.

Efficacy and safety of deep brain stimulation in mesencephalic locomotor region for motor function in patients with post-stroke hemiplegia: a study protocol for a multi-center double-blind crossover randomized controlled trial.

Deep brain stimulation (DBS) is a potential treatment for improving movement disorder. However, few large-sample studies can reveal its efficacy and safety. This study aims to initially explore the efficacy and safety of DBS in the mesencephalic locomotor region (MLR) on motor function in patients with post-stroke hemiplegia. This multicenter, prospective, double-blind, randomized crossover clinical trial aims to assess the safety and effectiveness of Deep Brain Stimulation (DBS) in the mesencephalic locomotor region (MLR) for patients with moderate to severe post-stroke hemiplegia. Sixty-two patients with stable disease after a year of conservative treatment will be enrolled and implanted with deep brain electrodes. Post-surgery, patients will be randomly assigned to either the DBS group or the control group, with 31 patients in each. The DBS group will receive electrical stimulation 1 month later, while the control group will undergo sham stimulation. Stimulation will be discontinued after 3 and 6 months, followed by a 2-week washout period. Subsequently, the control group will receive electrical stimulation, while the DBS group will undergo sham stimulation. Both groups will resume electrical stimulation at the 9th and 12th-month follow-ups. Post-12-month follow-up, motor-related scores will be collected for analysis, with the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) as the primary metric. Secondary outcomes include balance function, neuropsychiatric behavior, fall risk, daily living activities, and quality of life. This study aims to provide insights into the therapeutic benefits of DBS for post-stroke hemiplegia patients. We proposed this study for the first time to comprehensively explore the effectiveness and safety of DBS in improving motor function for post-stroke hemiplegia, and provide evidence for DBS in the treatment of post-stroke hemiplegia. Study limitations are related to the small sample size and short study period. Clinicaltrials.gov, identifier NCT05968248.

Edward O. Bixler, PhD: from the Apollo project and chimpanzees to sleep epidemiology.

What an honor to write about Dr. Edward O. Bixler's contributions to the sleep field. In 1967, Dr. Bixler published a case report on a chimpanzee with implanted brain electrodes while working at an Air Force base in New Mexico. A few years later, in 1971, he published on the sleep effects of flurazepam in individuals with insomnia together with Dr. Anthony Kales, data that he had collected when the Sleep Research & Treatment Center (SRTC) was housed at the University of California Los Angeles. Dr. Bixler, a meticulous scientist, learned from Dr. Kales, a devoted clinician, to study "the whole patient, and all aspects of sleep," a legacy that continued when the SRTC moved to Penn State in Hershey. Indeed, Dr. Bixler's tenure at Penn State from 1971 until 2019 kept the science of the SRTC focused on that premise and helped translate scientific evidence into clinical care. He not only contributed early to the pharmacology of sleep and the effects of hypnotics, but he was also a pioneer in "sleep epidemiology." His "Prevalence of sleep disorders in the Los Angeles metropolitan area" study of 1979 was the first rigorous epidemiological study on sleep disturbances. Starting in 1990, he established the Penn State Adult Cohort to estimate the prevalence and natural history of sleep-disordered breathing and other sleep disorders in adults. Inspired by life-course epidemiology, he established in 2001 the Penn State Child Cohort to estimate the same phenomena in children. This Living Legend paper captures and highlights Dr. Bixler's enduring legacy to sleep science.

Review of the targeting accuracy of frameless and frame-based robot-assisted deep brain stimulation electrode implantation in pediatric patients using the Neurolocate module.

The Neurolocate module is a 3D frameless patient registration module that is designed for use with the Neuromate stereotactic robot. Long-term electrical stimulation of the globus pallidus internus (GPi) and subthalamic nucleus (STN) via deep brain electrode implantation is particularly successful in a select group of movement disorders in pediatric patients. This study aimed to review the targeting accuracy of deep brain stimulation (DBS) electrode implantation in a single center, comparing standard frame-based techniques to the frameless Neurolocate module. Twenty-four pediatric patients underwent implantation of bilateral DBS electrodes under general anesthesia during the period of August 2018-August 2022. All patients underwent robot-assisted stereotactic implantation of DBS electrodes using an intraoperative O-arm 3D scanner to confirm the final electrode position. These coordinates were compared with the planned entry and target, with attention to depth, radial, directional, and absolute errors, in addition to Euclidean distance (ED). The primary outcome evaluated the accuracy and safety of the Neurolocate frameless technology compared with standard frame-based techniques. Of the 24 bilateral DBS electrode implantations performed, 62.5% used Neurolocate technology: 87.5% were delivered to the GPi and the remaining 12.5% to the STN. The mean patient age was 11.0 (range 4-18) years and 70.8% were male. The median absolute errors in x-, y-, and z-axes were 0.35, 0.75, and 0.9 mm, respectively, using the Neurolocate module compared with 0.30, 0.95, and 1.1 mm using the standard frame-based technique. The median ED from the planned target to the actual electrode position with the Neurolocate module was 1.28 mm versus 1.69 mm using standard frame-based techniques. No major perioperative complications occurred. Stereotactic robot-assisted DBS implantation with the frameless Neurolocate module is safe for use in the pediatric population, showing good surgical accuracy and no inferiority to standard frame-based techniques. The Neurolocate module for robotic DBS surgery has the potential to improve surgical targeting accuracy, surgical time, patient comfort, and safety.

DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps

Background:Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. New Methods:We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. Results:On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. Comparison with existing methods:In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. Conclusions:DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.

EEG-Based Multi-Frequency Multilayer Network for Exploring the Brain State Evolution Underlying Motor Imagery

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for the limb rehabilitation of stroke patients. However, due to the inadequate cognition of brain state during MI, the further application of BCI in the field of rehabilitation medicine is limited. In this paper, we focus on the MI tasks of four commonly used upper limb rehabilitation actions. And a complex network based topological analysis method is developed to characterize the brain state evolution mechanism over time. In detail, we first carry out the MI experiments and acquire the EEG signals associated with imagining left/right fist clenching and left/right wrist dorsiflexion. Then a multi-frequency multilayer (MFML) network is constructed, which maps the topological features of four MI-related frequency bands. The brain electrodes are defined as network nodes, and each frequency band corresponds to one single layer. Global clustering coefficient ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GC</i> ) is introduced to conduct quantitative topological analysis. Experimental results show that the topological structures of brain tend to be more energy consuming over time, suggesting that the cognitive demands of subjects gradually increase during MI. At the same time, it is found that continuous MI experiments improve the mind control ability of subjects, and the distinguishability of MI signals also gradually improves. Our research quantitatively explores the evolution of brain states behind MI, which is expected to guide the follow-up application of MI-BCI in clinical rehabilitation medicine.

Transfer function approaches for SEEG brain electrode interface applied to tissue classification

This paper is about transfer function approaches for brain-electrode interface modelling in the context of StereoElectroEncephaloGraphy, and their possible use in tissue classification (between grey and white matter). Monopolar and bipolar configurations are first reviewed, giving rise to possible nonparametric and parametric identification methods, as well as related possible classification results (for identical tissues and distinct tissues at measurement points, respectively). A method combining both approaches is then proposed, so as to end up with a classification at each measurement point in any case. The proposed methodology is implemented with clinical data collected from a set of epileptic patients, confirming its interest by providing more than 70% of accuracy in the obtained results.

EEG recognition model based on wavelet packet decomposition and ConvNet for art design decisions

Aiming at the subjective and unquantifiable problems in art design decisions, using EEG (electroencephalogram) to explain individual behavior is an effective solution. However, the accuracy of the traditional method is lower because of the complexity of EEG. Therefore, we propose the WPD-ConvNet recognition model based on wavelet packet decomposition and convolutional networks to enhance the recognition accuracy of EEG. First, subjects wear brain electrode caps to watch four art designs with different styles and need to make decisions. The EEG is collected during this process. Then, we use the WPD to obtain the features of EEG signals from the decomposed optimal subband nodes. Compared with FFT, WPD divides the frequency band through multiple levels to obtain more EEG time-frequency features related to art design decisions. Finally, we construct an convolutional neural network to identify these features. The experimental results show that our proposed EEG recognition model can achieve an accuracy of 88.7%, which is about 10% higher than others.

Cyst Formation following DBS Electrode Placement: A Rare Complication (P5-11.011)

<h3>Objective:</h3> To present two cases of intraparenchymal cysts, a rare complication of deep brain stimulation (DBS) in patients with movement disorders. <h3>Background:</h3> Formation of an intraparenchymal cyst following DBS electrode placement is a very rare complication. Only a few case reports exist and as a result, there is paucity of knowledge on its etiology, pathogenesis, prevention and treatment. <h3>Design/Methods:</h3> A chart review was done on two cases that had intraparenchymal cyst formation following DBS electrode placement at our medical center. In addition, we reviewed the literature to compare with our cases. <h3>Results:</h3> Patient A is a 79 y.o. male who underwent a left VIM thalamic lead placement and Patient B is a 64 y.o. underwent bilateral VIM lead placement for essential tremor. Both patients had a period of improvement in tremor before starting to experience new neurological deficits about 10 months and 2 months after lead placement, respectively. Patient A experienced right foot weakness, right arm weakness, and subjective decline in cognition while Patient B experienced an acute episode of altered mental status in the emergency department while under cardiac monitoring. Upon imaging, a cyst was found at the tip of the DBS lead in Patient A’s left thalamus and Patient B’s right thalamus. Infectious workup was negative indicating the cysts to be non-infectious. The brain electrodes were removed in both patients with resolution of neurological deficits and cyst regression on repeat imaging. Patient A continued to have tremor suppression for a year despite electrode removal while Patient B had an immediate tremor return to the pre-DBS baseline. <h3>Conclusions:</h3> Electrode explanation resulted in cyst regression with resolution of right hemi-body weakness in Patient A and altered mental status in Patient B. There is a need for reporting this very rare complication of DBS surgery to better understand its etiology, pathogenesis, treatment and prognosis. <b>Disclosure:</b> Ms. Naing has nothing to disclose. Dr. Venkataraman has nothing to disclose. The institution of Dr. Tatter has received research support from Monteris Medical, Inc. The institution of Dr. Tatter has received research support from Arbor Pharmaceuticals. Dr. Tatter has received intellectual property interests from a discovery or technology relating to health care. Adrian Laxton has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Monteris. Adrian Laxton has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Monteris . Dr. Siddiqui has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Boston Scientific Neuromodulation. Dr. Siddiqui has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Abbvie. The institution of Dr. Siddiqui has received research support from Boston Scientific Neuromodulation. The institution of Dr. Siddiqui has received research support from Abbvie. The institution of Dr. Siddiqui has received research support from Biogen. The institution of Dr. Siddiqui has received research support from Neuraly. The institution of Dr. Siddiqui has received research support from Abbvie.

512 Mapping High Gamma Neural Signals in Hand Primary Somatosensory Cortex Using a Multi-Thousand Channel Platinum Nanorod Microelectrode Grid

INTRODUCTION: Neurological deficits secondary to pathologies such as stroke, tumor, trauma, and neurodegeneration lead to enduring disability for many patients. Therefore, the development of neurorestorative technologies utilizing brain computer interfaces (BCIs) is an important goal in applied neuroscience. The high-gamma electrocorticographic (ECoG) neural signals from surgically implanted brain electrodes can be used as an indirect proxy for neuronal spiking activity and local neuronal output, which has translated into an effective control signal for BCIs. Direct electrical stimulation (DES) of primary somatosensory cortex has been used to provide sensory feedback in bi-directional sensorimotor BCIs (BBCIs). METHODS: During an awake craniotomy for tumor resection, a PtNRGrid was placed over primary hand somatosensory cortex while touches were applied to the ipsilateral and contralateral hand and arm. RESULTS: Here, we demonstrate the mapping of the high gamma signal in single digit and individual carpal distribution. CONCLUSIONS: This is the first analysis of high gamma signal in human hand primary somatosensory cortex utilizing PtNRGrids.