Spatial representation by ramping activity of neurons in the retrohippocampal cortex.

Neurons in the retrohippocampal cortices play crucial roles in spatial memory. Many retrohippocampal neurons have firing fields that are selectively active at specific locations, with memory for rewarded locations associated with reorganization of these firing fields. Whether this is the sole strategy for representing spatial memories is unclear. Here, we demonstrate that during a spatial memory task retrohippocampal neurons encode location through ramping activity that extends across segments of a linear track approaching and following a reward, with the rewarded location represented by offsets or switches in the slope of the ramping activity. Ramping representations could be maintained independently of trial outcome and cues marking the reward location, indicating that they result from recall of the track structure. When recorded in an open arena, neurons that generated ramping activity during the spatial memory task were more numerous than grid or border cells, with a majority showing spatial firing that did not meet criteria for classification as grid or border representations. Encoding of rewarded locations through offsets and switches in the slope of ramping activity also emerged in recurrent neural network models trained to solve a similar spatial memory task. Impaired performance of model networks following disruption of outputs from ramping neurons is consistent with this coding strategy supporting navigation to recalled locations of behavioral significance. Our results suggest that encoding of learned spaces by retrohippocampal networks employs both discrete firing fields and continuous ramping representations. We hypothesize that retrohippocampal ramping activity mediates readout of learned models for goal-directed navigation.

Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface.

OBJECTIVE: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. METHODS: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. RESULTS: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.

A bioelectric neural interface towards intuitive prosthetic control for amputees.

Objective. While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.Approach. Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention.Main results. A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention.Significance. Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

Lower-body posture estimation with a wireless smart insole.

Typical optical marker and camera based systems for motion capture suffer from several limitations. They are restricted to indoor environments, have difficulties tracking multiple people simultaneously and require expensive camera setups. In this work, we present a new method for lower-body posture estimation with a wireless smart insole using end-to-end training of a deep neural network. Our model is able to predict the movement of the entire lower body (including the hip, knee, ankle and toe) accurately in a wide range of activities. Inference only takes 1.62ms and hence can be used in real-time. The proposed method can potentially provide a very efficient and portable solution for applications like sports analysis, rehabilitation and virtual reality.

Human motor decoding from neural signals: a review.

Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

BACKGROUND: Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. NEW METHOD: In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. RESULTS: Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. COMPARISON WITH EXISTING METHODS: Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. CONCLUSIONS: A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels.

FreeWalker: a smart insole for longitudinal gait analysis.

Gait analysis is an important diagnostic measure to investigate the pattern of walking. Traditional gait analysis is generally carried out in a gait lab, with equipped force and body tracking sensors, which needs a trained medical professional to interpret the results. This procedure is tedious, expensive, and unreliable and makes it difficult to track the progress across multiple visits. In this paper, we present a smart insole called FreeWalker, which provides quantitative gait analysis outside the confinement of traditional lab, at low- cost. The insole consists of eight pressure sensors and two motion tracking sensors, i.e. 3-axis accelerometer and 3-axis gyroscope. This enables measurement of under-foot pressure distribution and motion sequences in real-time. The insole is enabled with onboard SD card as well as wireless data transmission, which help in continuous gait-cycle analysis. The data is then sent to a gateway, for analysis and interpretation of data, using a user interface where gait features are graphically displayed. We also present validation result of a subject's left foot, who was asked to perform a specific task. Experiment results show that we could achieve a data-sampling rate of over 1 KHz, transmitting data up to a distance of 20 meter and maintain a battery life of around 24 hours. Taking advantage of these features, FreeWalker can be used in various applications, like medical diagnosis, rehabilitation, sports and entertainment.

EC-PC spike detection for high performance brain-computer interface.

Spike detection is often the first step in neural signal processing. It has profound effects on subsequent steps down the signal processing pipeline. Most existing spike detection algorithms require manual setting of detection threshold, which is very inconvenient for long-term neural interface. Furthermore, these algorithms are usually only evaluated using simulated dataset. Few studies are devoted to evaluating how different spike detection algorithms affect decoding performance in brain-computer interface. We have proposed a new spike detection algorithm called "exponential component - power component" (EC-PC) that offers fully automatic unsupervised spike detection. In this study, we compared the performance of a motor decoding task when different spike detection algorithms were used. EC-PC is shown to produce a higher decoding accuracy compared with other existing algorithms. Our results suggest that EC-PC can help improve motor decoding performance of brain-computer interface.

A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study.

The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.

Performance of common spatial pattern under a smaller set of EEG electrodes in brain-computer interface on chronic stroke patients: a multi-session dataset study.

Brain-computer interface (BCI) uses non-muscular channel of the nervous system for communication. Common Spatial Pattern (CSP) is a popular spatial filtering method used to reduce the effect of volume conduction on EEG signals. It is thought that CSP requires a large number of electrodes to be effective. Using a 20-session dataset of motor imagery BCI usage by 5 stroke patients, we demonstrated that after channel selection, CSP can still maintain a high accuracy with low number of electrodes using a newly proposed channel selection method called CSP-rank (higher than 90% with 8 electrodes). The results showed that using only the first session for channel selection, a high accuracy can be maintained in subsequent sessions. CSP-rank has been compared to the popular support vector machine recursive feature elimination (SVM-RFE). The results showed that the CSP-rank required less electrodes to maintain accuracy higher than 90% (a minimum of 8 compared to 12 of SVM-RFE) and it attained a higher maximum accuracy (91.7% compared with 90.7% of SVM-RFE). This could support clinicians to apply more BCI in routine rehabilitation.

The effects of voluntary, involuntary, and forced exercises on motor recovery in a stroke rat model.

Stroke rehabilitation with different exercise paradigms has been investigated, but a comparison study on motor recovery after voluntary, involuntary, and forced exercises is limited. The current study used a rat brain ischemia model to investigate the effects of voluntary wheel running, involuntary muscle movement caused by functional electrical stimulation (FES), and forced treadmill exercise on motor recovery and brain BDNF changes. The results showed that voluntary exercise is the most effective intervention in upregulating the hippocampal BDNF level, and facilitating motor recovery after brain ischemia.

Comparing the multifocal electroretinogram topography before and after cataract surgery.

PURPOSE: To investigate whether multifocal electroretinogram (mfERG) topography would be affected by nuclear cataract. METHODS: Multifocal electroretinograms were recorded from 10 elderly subjects (10 eyes) with nuclear cataract of grade five (LOCS III) before and after cataract surgery (phacoemulsification). Their visual acuity before the cataract surgery was between 6/12 and 6/18. The postoperative period was from 2 to 3 months. None of the subjects had any significant eye disease apart from cataract. The mfERG responses were grouped into six concentric rings for analysis. Both the amplitudes and the latencies of N1 and P1 of first-order responses were used for analysis. RESULTS: N1 amplitude only from ring 1 increased significantly after cataract surgery. P1 amplitude from ring 1 and ring 2 also increased significantly after cataract surgery. The latencies of neither N1 nor P1 from all rings changed significantly. CONCLUSIONS: Nuclear cataract could affect the topography of mfERG, so clinicians should be aware of the effects of cataract when interpreting mfERG responses in cataract patients.