Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting

DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation

Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. In this paper, a 2.5D joint-view convolutional neural network (2.5D CNN) is proposed and evaluated for the identification of the OPZ in the cochlear segments. The proposed network consists of 2 complementary sagittal and bird-view (or top view) networks for the 3D OPZ recognition, each utilizing a ResNet-8 architecture consisting of 5 convolutional layers with rectified nonlinearity unit (ReLU) activations, followed by average pooling with size equal to the size of the final feature maps. The last fully connected layer of each network has 4 indicators, equivalent to the classes considered: the distance to the adjacent left and right walls, collision probability and heading angle. To demonstrate this, the 2.5D CNN was trained using a parametric data generation model, and then evaluated using anatomically constructed cochlea models from the micro-CT images of different cases. Prediction of the indicators demonstrates the effectiveness of the 2.5D CNN, for example the heading angle has less than 1° error with computation delays of less that <1 milliseconds

Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection

In Parkinson’s disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction and classification algorithms that have been used in brain machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves mean accuracy measures of classification accuracy 99.29%, F1-score of 97.90% and a choice probability of 99.86%

Towards an Improved Model for 65-nm CMOS at Cryogenic Temperatures

Cryogenic CMOS is a crucial subcomponent of quantum-technological applications, particularly as control electronics for quantum computers. Simulation is an important first step in designing any CMOS circuit. However, the standard BSIM4.5 model is only applicable for temperatures between 230 K and 420 K. In this work, N-type MOSFETs with different dimensions in a 65-nm CMOS technology were characterized at room temperature and liquid helium temperature (4.2 K). These measurements were compared with corresponding simulations from the BSIM4.5 model. A model of drain current in the triode region was constructed, where key parameters, such as threshold voltage and effective mobility, were modified. By adjusting these temperature-dependent parameters, the modified model predicted the triode region currents with an error reduced to 7.6%. Thus, the modified model can be utilized to simulate transistor behavior in the triode region at cryogenic temperatures

Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask

Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compac..

A Deep Neural Network-Based Spike Sorting with Improved Channel Selection and Artefact Removal

In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multi-unit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts

Generation of Anatomically Inspired Human Airway Tree Using Electrical Impedance Tomography: A Method to Estimate Regional Lung Filling Characteristics

The purpose of lung recruitment is to improve and optimize the air exchange flow in the lungs by adjusting the respiratory settings during mechanical ventilation. Electrical impedance tomography (EIT) is a monitoring tool that allows to measure regional pulmonary filling characteristics or filling index (FI) during ventilation. The conventional EIT system has limitations which compromise the accuracy of the FI. This paper proposes a novel and automated methodology for accurate FI estimation based on EIT images of recruitable regional collapse and hyperdistension during incremental positive end-expiratory pressure. It identifies details of the airway tree (AT) to generate a correction factor to the FIs providing an accurate measurement. Multiscale image enhancement followed by identification of the AT skeleton with a robust and self-exploratory tracing algorithm is used to automatically estimate the FI. AT tracing was validated using phantom data on a ground-truth lung. Based on generated phantom EIT images, including an established reference, the proposed method results in more accurate FI estimation of 65% in all quadrants compared with the current state-of-the-art. Measured regional filling characteristics were also examined by comparing regional and global impedance variations in clinically recorded data from ten different subjects. Clinical tests on filling characteristics based on extraction of the AT from the resolution enhanced EIT images indicated a more accurate result compared with the standard EIT images

A discrete wavelet transform-based voice activity detection and noise classification with sub-band selection

A real-time discrete wavelet transform-based adaptive voice activity detector and sub-band selection for feature extraction are proposed for noise classification, which can be used in a speech processing pipeline. The voice activity detection and sub-band selection rely on wavelet energy features and the feature extraction process involves the extraction of mel-frequency cepstral coefficients from selected wavelet sub-bands and mean absolute values of all sub-bands. The method combined with a feedforward neural network with two hidden layers could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. In comparison to the conventional short-time Fourier transform-based technique, it has higher F1 scores and classification accuracies (with a mean of 0.916 and 90.1%, respectively) across five different noise types (babble, factory, pink, Volvo (car) and white noise), a significantly smaller feature set with 21 features, reduced memory requirement, faster training convergence and about half the computational cost

Minimizing Stimulus Current in a Wearable Pudendal Nerve Stimulator Using Computational Models.

After spinal cord injury, functions of the lower urinary tract may be disrupted. A wearable device with surface electrodes which can effectively control the bladder functions would be highly beneficial to the patients. A trans-rectal pudendal nerve stimulator may provide such a solution. However, the major limiting factor in such a stimulator is the high level of current it requires to recruit the nerve fibers. Also, the variability of the trajectory of the nerve in different individuals should be considered. Using computational models and an approximate trajectory of the nerve derived from an MRI study, it is demonstrated in this paper that it may be possible to considerably reduce the required current levels for trans-rectal stimulation of the pudendal nerve compared to the values previously reported in the literature. This was corroborated by considering an ensemble of possible and probable variations of the trajectory. The outcome of this study suggests that trans-rectal stimulation of the pudendal nerve is a plausible long term solution for treating lower urinary tract dysfunctions after spinal cord injury