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Local field potential (LFP) recorded by sensing-enabled neurostimulators provided chronic observation of deep brain activities for the research of brain disorders. However, the contamination from the electrocardiogram (ECG) deteriorated the extraction of effective information from LFP. This study proposed a novel algorithm based on minimizing the variance combining template subtraction to improve the performance of ECG artifact removal for LFP. Four patients with implanted electrodes were recruited, and eight real LFP records were collected from their left and right hemispheres, respectively. The results showed that the proposed method improved the accuracy of artifact peak detection in LFP, and the subsequent signal quality after template subtraction compared to the traditional Pan-Tompkins (PT) method. The outcome of this study benefited the LFP-based brain research, promoting the application of sensing-enabled neurostimulators in more areas.

Deep brain stimulation (DBS) is an established treatment for patients with Parkinson’s disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep–wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8–13 Hz), beta (13–35 Hz), and gamma (35–50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns. [ABSTRACT FROM AUTHOR]

We report a scalable neural recording interface with embedded lossless compression to reduce dynamic power consumption ( \textPD ) for data transmission in high-density neural recording systems. We investigated the characteristics of neural signals and implemented effective lossless compression for local field potential (LFP) and extracellular action potential (EAP or spike) in separate signal paths. For LFP, spatial–temporal (spatiotemporal) correlation of the LFP signals is exploited in a \Delta -modulated \Delta \Sigma analog-to-digital converter ( \Delta -\Delta \Sigma ADC) and a dedicated digital difference circuit. Then, statistical redundancy is further eliminated through entropy encoding without information loss. For spikes, only essential parts of waveforms in the spikes are extracted from the raw data by using spike detectors and reconfigurable analog memories. The prototype chip was fabricated using 180-nm CMOS processes, incorporating 128 channels into a modular architecture that is easily scalable and expandable for high-density neural recordings. The fabricated chip achieved the data rate reduction for the LFPs and spikes by a factor of 5.35 and 10.54, respectively, from the proposed compression scheme. Consequently, was reduced by 89%, when compared to the uncompressed case. We also achieved the state-of-the-art recording performance of 3.37 \mu \text{W} per channel, 5.18 \mu V_{\mathrm {rms}}$ noise, and 3.41 {\text {NEF}}^{2}V_{\mathrm {DD}} . [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%). [ABSTRACT FROM PUBLISHER]

This paper presents a signal processing method for the electrophysiology simultaneously recorded during deep brain stimulation (DBS) as a research tool. Regarding the local field potential (LFP) signals recorded during stimulation, a novel method was proposed for removal of stimulation artifacts caused by the much stronger stimulating pulse compared to typical LFP. This artifact suppression method was tested and evaluated in an in vitro situation. The results indicate that the stimulation artifacts are well suppressed by this method. Secondly, this method was tested in vivo in Parkinson’s disease (PD) patients. It was used to process the LFP signals recorded intraoperatively from PD patients to preliminarily explore the quantitative dependencies of beta band synchronization variations in the subthalamic nucleus (STNs) on the applied DBS parameters, including stimulation voltage, frequency and pulse width. The results confirm that DBS therapy can suppress excessive beta frequency activity and that the degree of attenuation increases with increasing DBS voltage within a range of 1–3 V and increasing DBS frequency within a range of 60–120 Hz. The proposed artifact suppression method provides technical support for exploring the direct effect of electrical stimulation on the brain activities. [ABSTRACT FROM PUBLISHER]

Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, a 256-channel neural sensing microsystem with a sensing area of 5 × 5 mm 2 is presented based on 2.5-D through-silicon-via (TSV) integration. This microsystem composes of dissolvable μ-needles, TSV-embedded μ-probes, 256-channel neural amplifiers, 11-bit area-power-efficient successive approximation register analog-to-digital converters, and serializers. This microsystem can detect 256 electrocorticography and local field potential signals within a small area of 5 mm × 5 mm. The neural amplifier realizes 57.8 dB gain with only 9.8 μW per channel. The overall power of this microsystem is only 3.79 mW for 256-channel neural sensing. A smaller microsystem with dimension of 6 mm × 4 mm has been also implanted into rat brain for somatosensory evoked potentials (SSEPs) recording by using contralateral and ipsilateral electrical stimuli with intensity from 0.2 to 1.0 mA, and successfully observed different SSEPs from left somatosensory cortex of a rat. [ABSTRACT FROM PUBLISHER]

When analyzing brain activity such as local field potentials, it is often desired to represent neural events by stereotypic waveforms. Due to the nondeterministic nature of the neural responses, an adequate waveform estimate typically requires recording multiple repetitions of the neural events. It is common practice to segment the recorded signal into event-related epochs and calculate their average. This approach suffers from two major drawbacks: epoching can be problematic, especially in the case of overlapping neural events, and variability of the neural events across epochs (such as varying onset latencies) is not accounted for, which may lead to a distorted average. In this paper, we propose a novel method called adaptive waveform learning (AWL). It is designed to learn multicomponent representations of neural events while explicitly capturing and compensating for waveform variability, such as changing latencies or more general shape variations. Thanks to its generality, it can be applied to both epoched (i.e., segmented) and continuous (i.e., nonepoched) signals by making the corresponding specializations to the algorithm. We evaluate AWL's performance and robustness to noise on simulated data and demonstrate its empirical utility on an electrophysiological recording containing intracranial epileptiform discharges (epileptic spikes). [ABSTRACT FROM PUBLISHER]

Although closed-loop deep brain stimulation (DBS) promises treatment of many neurological disorders, an implantable system-on-chip (SoC) implementing an effective closed-loop DBS algorithm has not been demonstrated. This work introduces a logarithmic, closed-loop DBS system that detects and processes low-frequency brain field signals to control and adapt stimulation currents. The system records and processes neural signals with four low-noise neural amplifier (LNA) channels, a multiplexed logarithmic ADC, and two high-pass and two low-pass digital logarithmic filters. Logarithmic processing saves power and achieves high dynamic range. A logarithmic domain digital signal processor (DSP) and PI-controller controls eight current stimulator channels and enables closed-loop stimulation. An RF transceiver, a clock generator, and a power harvester are also included in the system to achieve a complete implantable SoC. The 4 mm^2 180 nm CMOS prototype consumes a total of 468 \muW for recording and processing neural signals, for stimulation, and for two-way wireless communication. [ABSTRACT FROM AUTHOR]

The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals. Nonlinear Interdependence (NLI) analysis is an effective method for measuring synchronization direction and strength of bivariate neural signal. However, the method currently does not directly apply in handling multivariate signal. Its application in practice has also long been largely hampered by the ultra-high complexity of NLI algorithms. Aiming at these problems, this study 1) extends the conventional NLI to quantify the global synchronization of multivariate neural signals, and 2) develops a parallelized NLI method with general-purpose computing on the graphics processing unit (GPGPU), namely, G-NLI. The approach performs synchronization measurement in a massively parallel manner. The G-NLI has improved the runtime performance by more than 1000 times comparing to the original sequential NLI. Meanwhile, the G-NLI was employed to analyze 10-channel local field potential (LFP) recordings from a patient suffering from temporal lobe epilepsy. The results demonstrate that the proposed G-NLI method can support real-time global synchronization measurement and it could be successful in localization of epileptic focus. [ABSTRACT FROM AUTHOR]

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements, while SUA, multispike trains (MSP), multiunit activity, and local field potential time (LFPrms), and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20–60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints. [ABSTRACT FROM PUBLISHER]

Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data. [ABSTRACT FROM PUBLISHER]

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity (MUA), as well as low-frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode 3-D reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of seven different LFP frequency bands in the range of 0.3-400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g., 100-200 and 200-400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broad-band high frequency LFPs, likely reflecting MUA, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture, and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics. [ABSTRACT FROM AUTHOR]

Bistable perception arises when an ambiguous stimulus under continuous view is perceived as an alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper, we focus on extracting the percept-related features from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-from-motion (SFM) perception. Our proposed feature extraction approach consists of two stages. First, we estimate and remove from each LFP trial the nonpercept-related stimulus-evoked activity via a local regression method called the locally weighted scatterplot smoothing because of the dissociation between the perception and the stimulus in our experimental paradigm. Second, we use the common spatial patterns approach to design spatial filters based on the residue signals of multiple channels to extract the percept-related features. We exploit a support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We apply the proposed approach to the multichannel intracortical LFP data collected from the middle temporal (MT) visual cortex in a macaque monkey performing an SFM task. We demonstrate that our approach is effective in extracting the discriminative features of the percept-related activity from LFP and achieves excellent decoding performance. We also find that the enhanced gamma band synchronization and reduced alpha and beta band desynchronization may be the underpinnings of the percept-related activity. [ABSTRACT FROM AUTHOR]

Abstract-Cognitive functions are often studied by recording electric potentials from the brain over repeated presentations of a sensory stimulus or repeated performance of a motor action. Each repetition is called a trial. Recent work has demonstrated that contrary to the traditional view, the event-related potential (ERP) can vary from trial to trial and the background ongoing activity often contains rich information about the cognitive state of the brain. Based on such a variable signal plus ongoing activity model, an iterative parameter estimation method is proposed in which both the single-trial parameters of the ERP and the autoregressive representation of the ongoing activity are obtained simultaneously. This technique, referred to as the analysis of single-trial ERP and ongoing activities method, is first tested on simulation examples, and then applied to the local field potential recordings from monkeys performing a visuomotor task. [ABSTRACT FROM AUTHOR]

Abstract-Bistable perception refers to the phenomenon of spontaneously alternating percepts while viewing the same stimulus continuously. Bistable stimuli allow dissociation between stimuli and perception, and thus, provide a unique opportunity for understanding the neural basis of visual perception. In this paper, we focus on a relaxation (RELAX) based algorithm to select features from the multitaper spectral estimates of the multichannel intracortical local field potential (LFP), simultaneously collected from the middle temporal visual cortex of a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We demonstrate that RELAX surpasses the conventional sequential forward selection (SFS) by offering the flexibility of modifying selected features. We propose a redundancy reduction preprocessing technique to significantly reduce the computational load for both SFS and RELAX. We exploit the support vector machines classifier based on the selected features for single-trial decoding the reported perception. Our results demonstrate the excellent performance of the RELAX feature selection algorithm. Furthermore, we find that the features in the gamma frequency band (30-100 Hz) of LFP are most relevant to bistable SFM perception. This finding is novel in awake monkey studies and suggests that gamma oscillations carry the most discriminative information for bistable perception of SFM stimuli. [ABSTRACT FROM AUTHOR]

In this paper, intracortical local field potentials (LFPs) and single units were recorded from the motor cortices of monkeys (Macaca fascicularis) while they preformed a standard three-dimensional (3-D) center-out reaching task. During the center-out task, the subjects held their hands at the location of a central target and then reached to one of eight peripheral targets forming the corners of a virtual cube. The spectral amplitudes of the recorded LFPs were calculated, with the high-frequency LFP (HF-LFP) defined as the average spectral amplitude change from baseline from 60 to 200 Hz. A 3-D linear regression across the eight center-out targets revealed that approximately 6% of the beta LFPs (18-26 Hz) and 18% of the HF-LFPs were tuned for velocity (p-value <0.05), while 10% of the beta LFPs and 15% of the HF-LFPs were tuned for position. These results suggest that a multidegree-of-freedom brain-machine interface is possible using high-frequency LFP recordings in motor cortex. [ABSTRACT FROM AUTHOR]