Publications
14 results found
Hyanda MH, Ahmadi N, Charlton PH, et al., 2023, A Comparative Evaluation of Video Codecs for rPPG-based Heart Rate Estimation, Pages: 243-247
Remote photoplethysmography (rPPG) is a noninvasive technology that can be used to extract heart rate (HR) from video data. Video codecs are typically used for compressing video, which allows for efficient data storage, but could negatively impact the accuracy of HR estimation. To date, only limited number of studies investigate the impact of video codecs on rPPG-based HR estimation. In this paper, we present a comprehensive comparative evaluation of video codecs for rPPG-based HR estimation. Several factors being compared include video encoders, video containers, quality rate mode, and resolution. Experimental results using in-house dataset show that the lossy encoder like VP9 yield good performance nearly similar to the lossless counterparts. Additionally, the.avi container is preferable because it can contain all encoders tested except x265 (HEVC). Varying the resolution significantly affects the file size and accuracy of HR estimation. This study provides useful information for the creation of rPPG dataset and development of rPPG algorithm.
Ahmadi N, Adiono T, Purwarianti A, et al., 2022, Improved spike-based brain-machine interface using bayesian adaptive kernel smoother and deep learning, IEEE Access, Vol: 10, Pages: 29341-29356, ISSN: 2169-3536
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.
Ahmadi N, Constandinou T, Bouganis C, 2021, Inferring entire spiking activity from local field potentials, Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322
Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) andspikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can beinferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based techniquewhich may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referredto as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to betterperformance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim toaddress this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performingdifferent tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPswith good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUAand MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate thatLFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spikerelationship and for the development of LFP-based BMIs.
Ahmadi N, Constandinou TG, Bouganis C-S, 2021, Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning, Journal of Neural Engineering, Vol: 18, Pages: 1-23, ISSN: 1741-2552
Objective. Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach. We propose entire spiking activity (ESA)—an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique—as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.
Ahmadi N, Constandinou T, Bouganis C-S, 2021, Impact of referencing scheme on decoding performance of LFP-based brain-machine interface, Journal of Neural Engineering, Vol: 18, ISSN: 1741-2552
OBJECTIVE: There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. APPROACH: To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. MAIN RESULTS: Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning
<jats:p>Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.</jats:p>
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Impact of referencing scheme on decoding performance of LFP-based brain-machine interface
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions.</jats:p></jats:sec><jats:sec><jats:title>Significance</jats:title><jats:p>Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.</jats:p></jats:sec>
Ahmadi N, Constandinou TG, Bouganis C-S, 2020, Inferring entire spiking activity from local field potentials with deep learning
<jats:title>ABSTRACT</jats:title><jats:p>Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Understanding the relationship between these two signals is essential for gaining deeper insight into neuronal coding and information processing in the brain and is also relevant to brain-machine interface (BMI) research. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. These spiking activities that are typically extracted via threshold-based technique may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another spiking activity in the form of a continuous signal, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by employing a deep learning method to infer ESA from LFPs intracortically recorded from the motor cortex area of two monkeys performing different tasks. Results from long-term recording sessions and across different tasks revealed that the inference accuracy of ESA yielded consistently and significantly higher accuracy than that of SUA and MUA. In addition, local motor potential (LMP) was found to be the most highly predictive feature compared to other LFP features. The overall results indicate that LFPs contain substantial information about the spikes, particularly ESA, which could be useful for the development of LFP-based BMIs. The results also suggest the potential use of ESA as an alternative neuronal population activity measure for analysing neural responses to stimuli or behavioural tasks.</jats:p>
Ahmadi N, Constandinou TG, Bouganis C-S, 2019, End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 1-4, ISSN: 2163-4025
In recent years, local field potentials (LFPs) haveemerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstratedthat LFP-based BMIs could provide long-term recording stabilityand comparable decoding performance to their spike counter-parts. Despite the compelling results, however, most LFP-basedBMIs still make use of hand-crafted features which can betime-consuming and suboptimal. In this paper, we propose anend-to-end system approach based on temporal convolutionalnetwork (TCN) to automatically extract features and decodekinematics of hand movements directly from raw LFP signals.We benchmark its decoding performance against traditionalapproach incorporating long short-term memory (LSTM) de-coders driven by hand-crafted LFP features. Experimental re-sults demonstrate significant performance improvement of theproposed approach compared to the traditional approach. Thissuggests the suitability of TCN-based end-to-end system and itspotential for providng stable and high decoding performanceLFP-based BMIs.
Ahmadi N, Cavuto ML, Feng P, et al., 2019, Towards a distributed, chronically-implantable neural interface, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 719-724, ISSN: 1948-3546
We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing.
Ahmadi N, Constandinou TG, Bouganis C-S, 2019, Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Publisher: IEEE, Pages: 415-419
Local field potential (LFP) has gained increasing interest as an alternativeinput signal for brain-machine interfaces (BMIs) due to its informativefeatures, long-term stability, and low frequency content. However, despitethese interesting properties, LFP-based BMIs have been reported to yield lowdecoding performances compared to spike-based BMIs. In this paper, we propose anew decoder based on long short-term memory (LSTM) network which aims toimprove the decoding performance of LFP-based BMIs. We compare offline decodingperformance of the proposed LSTM decoder to a commonly used Kalman filter (KF)decoder on hand kinematics prediction tasks from multichannel LFPs. We alsobenchmark the performance of LFP-driven LSTM decoder against KF decoder drivenby two types of spike signals: single-unit activity (SUA) and multi-unitactivity (MUA). Our results show that LFP-driven LSTM decoder achievessignificantly better decoding performance than LFP-, SUA-, and MUA-driven KFdecoders. This suggests that LFPs coupled with LSTM decoder could provide highdecoding performance, robust, and low power BMIs.
Ahmadi N, Constandinou T, Bouganis C, 2018, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), PLoS ONE, Vol: 13, ISSN: 1932-6203
Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-mac
Ahmadi N, Constandinou TG, Bouganis C, 2018, Spike rate estimation using Bayesian Adaptive Kernel Smoother (BAKS) and its application to brain machine interfaces, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on of fline BMI decoding performance using Kalman filter. The results show that overlap BAKS improved the decoding performance up to 3.33% and 12.93% compared to overlap and non-overlapbinning methods, respectively, depending on the window size. This suggests the feasibility and the potential use of BAKS method for real-time BMIs.
Ahmadi N, Constandinou TG, Bouganis C-S, 2017, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)
<jats:title>Abstract</jats:title><jats:p>Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could als
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