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decoding the Nature of Emotion in the Brain

TRENDS IN COGNITIVE SCIENCES 2016年第6期20卷 444-455页

作者： Kragel, Philip A. LaBar, Kevin S. Duke Univ Dept Psychol & Neurosci Durham NC 27708 USA

A central, unresolved problem in affective neuroscience is understanding how emotions are represented in nervous system activity. After prior localization approaches largely failed, researchers began applying multivariate statistical tools to reconceptualize how emotion constructs might be embedded in large-scale brain networks. Findings from pattern analyses of neuroimaging data show that affective dimensions and emotion categories are uniquely represented in the activity of distributed neural systems that span cortical and subcortical regions. Results from multiple-category decoding studies are incompatible with theories postulating that specific emotions emerge from the neural coding of valence and arousal. This 'new look' into emotion representation promises to improve and reformulate neurobiological models of affect.

关键词： affect emotion fMRI multivariate pattern analysis neural decoding valence

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Hardware-Efficient Compression of neural Multi-Unit Activity

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IEEE ACCESS 2022年 10卷 117515-117529页

作者： Savolainen, Oscar W. W. Zhang, Zheng Feng, Peilong Constandinou, Timothy G. G. Imperial Coll London Dept Elect & Elect Engn South Kensington Campus London SW7 2AZ England Univ Surrey UK Dementia Res Inst UKDRI Imperial Coll London Care Res & Technol CR&T Ctr Guildford GU2 7XH England

Brain-machine interfaces (BMI) are tools for measuring neural activity in the brain, used to treat numerous conditions. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.

关键词： Brain-machine interfaces embedded systems FPGA Huffman encoding neural data compression neural decoding multi-unit activity real-time signal processing reconfigurable hardware

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Improved Spike-Based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning

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IEEE ACCESS 2022年 10卷 29341-29356页

作者： Ahmadi, Nur Adiono, Trio Purwarianti, Ayu Constandinou, Timothy G. Bouganis, Christos-Savvas Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Imperial Coll London Ctr Bioinspired Technol Inst Biomed Engn London SW7 2AZ England Bandung Inst Technol Sch Elect Engn & Informat Bandung 40132 Indonesia Bandung Inst Technol Ctr Artificial Intelligence U CoE AI VLB Bandung 40132 Indonesia Imperial Coll London Care Res & Technol Ctr UK Dementia Res Inst London SW7 2AZ England

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.

关键词： decoding Kernel Deep learning Estimation Signal processing algorithms Bayes methods Kinematics Brain-machine interface Bayesian adaptive kernel smoother deep learning firing rate estimation multiunit activity neural decoding

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Coupling time decoding and trajectory decoding using a target-included model in the motor cortex

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NEUROCOMPUTING 2012年 82卷 117-126页

作者： Lawhern, Vernon Hatsopoulos, Nicholas G. Wu, Wei Florida State Univ Dept Stat Tallahassee FL 32306 USA Univ Chicago Dept Organismal Biol & Anat Comm Computat Neurosci Chicago IL 60637 USA Univ Chicago Dept Organismal Biol & Anat Comm Neurobiol Chicago IL 60637 USA

Significant progress has been made within the last decade in motor cortical decoding that predicts movement behaviors from population neuronal activity in the motor cortex. A majority of these decoding methods have focused on estimating a subject's hand trajectory in a continuous movement. We recently proposed a time identification decoding approach and showed that if a stereotyped movement is well represented by a sequence of targets (or landmarks), then the main structure of the movement can be reconstructed by detecting the reaching times at those targets. Both trajectory decoding and landmark-time decoding have their particular advantages, whereas a coupling of these two different strategies has not been examined. In this article we propose a synergy that comes from combining these two approaches for a stereotyped movement under a linear state-space framework. We develop a new decoding procedure based on a forward-backward propagation where the target is used in the initial stage in the backward step. Experimental results show that the new method significantly improves decoding accuracy over the non-target-included models. Furthermore, the coupling based on the new target-included method effectively combines the time decoding and trajectory decoding and further improves the decoding accuracy. (c) 2011 Elsevier B.V. All rights reserved.

关键词： neural decoding Motor cortex Target-included model Time decoding Trajectory decoding State-space model

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Intracortical Hindlimb Brain-Computer Interface Systems: A Systematic Review

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IEEE ACCESS 2023年 11卷 28119-28139页

作者： Ghodrati, Mohammad Taghi Mirfathollahi, Alavie Shalchyan, Vahid Daliri, Mohammad Reza Iran Univ Sci & Technol IUST Sch Elect Engn Biomed Engn Dept Neurosci & Neuroengn Res Lab Tehran *** Iran Inst Cognit Sci Studies ICSS Tehran *** Iran

Brain-computer interfaces (BCI) can help people with motor disorders to regain their ability to communicate and interact with the surrounding environment. The majority of studies in this field pursue the development of BCI systems to enhance or restore the movement functionality of people with disability. Although the studies on the development of BCIs to restore hindlimb movements have shorter backgrounds compared to forelimb, several studies have investigated hindlimb BCIs and their results were promising. In the present study, we systematically reviewed the studies investigating the decoding of hindlimb movement parameters using intracortical signals. Three scientific databases (PubMed, Scopus, and Embase) were used to extract the articles and the experiment, recording, processing methods, and results of the included studies were discussed. Although several studies on upper-limb intracortical BCIs have been conducted on human subjects, almost all studies in hindlimb intracortical BCI field were performed on animal subjects. The most investigated task was walking on a treadmill, and the position of hindlimb joints and gait phase were the most studied continuous and discrete parameters, respectively. The included studies have mainly used spikes and linear decoders, which leaves the question of the effectiveness of using local field potentials and nonlinear decoders in this field unanswered. Although the results imply that hindlimb movement decoding using brain signals is feasible in laboratory conditions, further investigations are required to examine the hindlimb BCIs in real-life conditions.

关键词： decoding Legged locomotion Robots Databases Task analysis Recording Brain-computer interfaces Brain-computer interface motor cortex hindlimb neural decoding intracortical

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Reducing power requirements for high-accuracy decoding in iBCIs

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JOURNAL OF neural ENGINEERING 2024年第6期21卷 066001-066001页

作者： Karpowicz, Brianna M. Bhaduri, Bareesh Nason-Tomaszewski, Samuel R. Jacques, Brandon G. Ali, Yahia H. Flint, Robert D. Bechefsky, Payton H. Hochberg, Leigh R. AuYong, Nicholas Slutzky, Marc W. Pandarinath, Chethan Emory Univ Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Atlanta GA 30332 USA Northwestern Univ Dept Neurol Chicago IL USA Harvard Med Sch Massachusetts Gen Hosp Ctr Neurotechnol & Neurorecovery Dept Neurol Boston MA USA Providence VA Med Ctr Vet Affairs Rehabil Res & Dev Ctr Neurorestorat & Providence RI USA Brown Univ Robert J & Nancy D Carney Inst Brain Sci Providence RI USA Brown Univ Sch Engn Providence RI USA Emory Univ Dept Neurosurg Atlanta GA 30322 USA Emory Univ Dept Cell Biol Atlanta GA USA Northwestern Univ Dept Neurosci Chicago IL USA Northwestern Univ Dept Biomed Engn Evanston IL USA Northwestern Univ Dept Phys Med & Rehabil Chicago IL USA Shirley Ryan AbilityLab Chicago IL USA

Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors;however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes. Approach. Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech. Main results. In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes. Significance. Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.

关键词： brain-computer interfaces neural dynamics low power neural decoding

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Modeling and decoding motor cortical activity using a switching Kalman filter

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2004年第6期51卷 933-942页

作者： Wu, W Black, MJ Mumford, D Gao, Y Bienenstock, E Donoghue, JP Brown Univ Div Appl Math Providence RI 02912 USA Brown Univ Dept Comp Sci Providence RI 02912 USA Brown Univ Dept Neurosci Providence RI 02912 USA

We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.

关键词： mixture model motor cortex neural decoding neural prosthesis switching Kalman filter

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decoding visual input from V1 neuronal activity with particle filtering

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NEUROCOMPUTING 2004年 58卷 849-855页

作者： Kelly, R Lee, TS Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA

In this study, we investigated the use of particle filtering in reconstructing time-varying input visual signals based on Macaque V1 neurons' responses. A multitude of hypothesis particles are proposed for reconstructing the input stimulus up to time t. A prediction kernel (consisting of the first- and second-order forward Wiener kernels, derived by regression) is used to predict the neural response at time t based on the estimated input signals in the 200 ms prior to t. The fitness of this estimated response in predicting the measured response at time t is used to weigh the importance of the various hypotheses. The hypothesis particle space is collapsed by re-sampling over time. We find this method quite successful in reconstructing the input stimulus for 30 out of 33 V1 neurons measured. It out performs the optimal linear decoder that we have experimented with in the past (Neurocomputing, in press). (C) 2004 Elsevier B.V. All rights reserved.

关键词： particle filtering neural decoding

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A High Definition Noninvasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human With Tetraplegia

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2019年第4期66卷 910-919页

作者： Annetta, Nicholas V. Friend, Jeffrey Schimmoeller, Andrew Buck, Vimal S. Friedenberg, David A. Bouton, Chad E. Bockbrader, Marcia A. Ganzer, Patrick D. Colachis, Sam C. Zhang, Mingming Mysiw, W. Jerry Rezai, Ali R. Sharma, Gaurav Battelle Mem Inst Med Devices & Neuromodulat Grp 505 King Ave Columbus OH 43201 USA Battelle Mem Inst 505 King Ave Columbus OH 43201 USA Ohio State Univ Ctr Design & Mfg Excellence Columbus OH 43210 USA Battelle Mem Inst Hlth & Analyt Grp 505 King Ave Columbus OH 43201 USA Feinstein Inst Manhasset NY USA Ohio State Univ Neurol Inst Wexner Med Ctr Columbus OH 43210 USA Ohio State Univ Dept Phys Med & Rehabil Columbus OH 43210 USA Neurotechnol Innovat Translator Columbus OH USA Ohio State Univ Ctr Neuromodulat Columbus OH 43210 USA Ohio State Univ Neurol Inst Columbus OH 43210 USA West Virginia Univ Rockefeller Neurosci Inst Morgantown WV 26506 USA

Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. Methods: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a noninvasive neuromuscular electrical stimulation (NMES) system. Results: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. Conclusion: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. Significance: These results have implications for enabling complex rotary hand functions in sequence with other functionally relevant movements for patients suffering from SCI, stroke, and other sensorimotor dysfunctions.

关键词： Brain-computer interface intracortical microelectrode array functional electrical stimulation neural decoding neuromuscular electrical stimulation

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Brain-computer interface control along instructed paths

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JOURNAL OF neural ENGINEERING 2015年第1期12卷 016015页

作者： Sadtler, P. T. Ryu, S. I. Tyler-Kabara, E. C. Yu, B. M. Batista, A. P. Univ Pittsburgh Dept Bioengn Pittsburgh PA 15260 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Univ Pittsburgh Syst Neurosci Inst Pittsburgh PA USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Palo Alto Med Fdn Res Inst Palo Alto CA 94301 USA Univ Pittsburgh Dept Phys Med & Rehabil Pittsburgh PA USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA 15260 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA

Objective. Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire. Approach. We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. Main results. We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. Significance. The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.

关键词： brain-computer interface neural decoding motor cortex

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