Advancing robot-guided techniques in lumbar spine surgery: a systematic review and meta-analysis.

BACKGROUND: Lumbar spine surgery is a crucial intervention for addressing spinal injuries or conditions affecting the spine, often involving lumbar fusion through pedicle screw (PS) insertion. The precision of PS placement is pivotal in orthopedic surgery. This systematic review compares the accuracy of robot-guided (RG) surgery with free-hand fluoroscopy-guided (FFG), free-hand without fluoroscopy-guided (FHG), and computed tomography image-guided (CTG) techniques for PS insertion. METHODS: A systematic search of various databases from 1 January 2013 to 30 December 2023 was conducted following PRISMA guidelines. Primary outcomes, including PS insertion accuracy and breach rate, were analyzed using a random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: The overall accuracy of PS insertion using RG, based on 37 studies involving 3,837 patients and 22,117 PS, is 97.9%, with a breach rate of 0.021. RG demonstrated superior accuracy compared to FHG and CTG, with breach rates of 3.4 and 0.015 respectively for RG versus FHG, and 3.8 and 0.026 for RG versus CTG. Additionally, RG was associated with reduced mean estimated blood loss compared to CTG, indicating improved safety. CONCLUSIONS: The RG is associated with enhanced accuracy of PS insertion and reduced breach rates over other methods. However, additional randomized controlled trials comparing these modalities are needed for further validation. PROSPERO REGISTRATION: CRD42023483997.

Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOFs) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology, celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF finger kinematics simultaneously. The method's efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000 windows for training Kalman model parameters, the average computation time remains under 0.01 s. This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) to EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors.

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients' motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance- The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.

Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor.

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke rehab.Clinical Relevance: This study indicated that optimal selection of the ICLabel artifactual rejection threshold is essential for EEG enhancement for adequate signal characterization. Thus, a TH-values with a confidence level between 50% - 70% would be suggested for artifactual ICs rejection in MI-EEG.

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition.

Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects' thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals.

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines.

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control.

Introduction: Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question. Method: This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder. Result: The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.

Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems.

Surface electromyogram (sEMG) is arguably the most sought-after physiological signal with a broad spectrum of biomedical applications, especially in miniaturized rehabilitation robots such as multifunctional prostheses. The widespread use of sEMG to drive pattern recognition (PR)-based control schemes is primarily due to its rich motor information content and non-invasiveness. Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with inevitable interferences that distort intrinsic characteristics of the signal, precluding existing signal processing methods from yielding requisite motor control information. Therefore, we propose a multiresolution decomposition driven by dual-polynomial interpolation (MRDPI) technique for adequate denoising and reconstruction of multi-class EMG signals to guarantee the dual-advantage of enhanced signal quality and motor information preservation. Parameters for optimal MRDPI configuration were constructed across combinations of thresholding estimation schemes and signal resolution levels using EMG datasets of amputees who performed up to 22 predefined upper-limb motions acquired in-house and from the public NinaPro database. Experimental results showed that the proposed method yielded signals that led to consistent and significantly better decoding performance for all metrics compared to existing methods across features, classifiers, and datasets, offering a potential solution for practical deployment of intuitive EMG-PR-based control schemes for multifunctional prostheses and other miniaturized rehabilitation robotic systems that utilize myoelectric signals as control inputs.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

Effective Evaluation of Finger Sensation Evoking by Non-Invasive Stimulation for Sensory Function Recovery in Transradial Amputees.

Synergetic recovery of both somatosensory and motor functions is highly desired by limb amputees to fully regain their lost limb abilities. The commercially available prostheses can restore the lost motor function in amputees but lack intuitive sensory feedback. The previous studies showed that electrical stimulation on the arm stump would be a promising approach to induce sensory information into the nervous system, enabling the possibility of realizing sensory feedback in limb prostheses. However, there are currently limited studies on the effective evaluation of the sensations evoked by transcutaneous electrical nerve stimulation (TENS). In this paper, a multichannel TENS platform was developed and the different stimulus patterns were designed to evoke stable finger sensations for a transradial amputee. Electroencephalogram (EEG) was recorded simultaneously during TENS on the arm stump, which was utilized to evaluate the evoked sensations. The experimental results revealed that different types of sensations on three phantom fingers could be stably evoked for the amputee by properly selecting TENS patterns. The analysis of the event-related potential (ERP) of EEG recordings further confirmed the evoked sensations, and ERP latencies and curve characteristics for different phantom fingers showed significant differences. This work may provide insight for an in-depth understanding of how somatosensation could be restored in limb amputees and offer technical support for the applications of non-invasive sensory feedback systems.

A Pilot Study on the Performance of Time-Domain Features in Speech Recognition based on high-density sEMG.

Features extracted from the surface electromyography (sEMG) signals during the speaking tasks play an essential role in sEMG based speech recognition. However, currently there are no general rules on the optimal choice of sEMG features to achieve satisfactory performance. In this study, a total of 120 electrodes were placed on the face and neck muscles to record the high-density (HD) sEMG signals when subjects spoke ten digits in English. Then ten different time-domain features were computed from the HD sEMG signals and the classification performance of the speech recognition was thoroughly compared. The contribution of each feature was examined by using three performance metrics, which include classification accuracy, sensitivity, and F1-Score. The results showed that, among all the ten different features, the features of WFL, MAV, RMS, and LOGD were considered to be superior because they achieved higher classification accuracies with high sensitivities and higher F1-Scores across subjects/trials in the sEMG-based digit recognition tasks. The findings of this study might be of great value to choose proper signal features that are fed into the classifier in sEMG-based speech recognition.

A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL's Motor Imagery Characterization.

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for brain-computer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method.Clinical Relevance- This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.

Towards optimal selection of stimuli polarity method for effective evoking auditory brainstem responses.

Towards eliminating stimulus artifacts, alternating polarity stimuli have been widely adopted in eliciting the auditory brainstem response. However, considering the difference in the physiologic basis of the positive and negative polarity stimuli on the auditory system, it is unclear whether alternating polarity stimuli would adversely affect the auditory brainstem response characteristics. This research proposes a new polarity method for stimulus artifacts elimination, Sum polarity, that separately utilized the rarefaction and condensation stimuli and then summed the two evoked responses. We compared the waveform morphology and latencies of the auditory brainstem responses evoked by familiar stimuli (including click, tone-burst, and chirp) with different polarity methods in normal-hearing subjects to investigate the new method's effectiveness. The experimental results showed that alternating polarity of the click and chirp had little effect on the auditory brainstem response. In contrast, alternating polarity affected the waveform morphology and latencies of the auditory brainstem responses to the low-frequency tone-burst, with the effect decreasing as the stimulus frequency increased. These results demonstrated the performance of any polarity method is related to the characteristics of the stimulus signal itself, and no polarity method is optimal for all types of stimuli. Based on the analysis of experimental results, a fixed polarity and alternating polarity were recommended for the click and chirp auditory brainstem responses, respectively. Furthermore, considering the apparent latency differences between the responses to opposite polarity stimuli, the Sum polarity was suggested for the tone-burst auditory brainstem responses. Moreover, this work verified the feasibility of the Sum polarity, which offers another choice for eliminating stimulus artifacts in an evoked potential acquisition.

A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding.

BACKGROUND AND OBJECTIVE: Non-invasive multichannel Electroencephalography (EEG) recordings provide an alternative source of neural information from which motor imagery (MI) patterns associated with limb movement intent can be decoded for use as control inputs for rehabilitation robots. The presence of multiple inherent dynamic artifacts in EEG signals, however, poses processing challenges for brain-computer interface (BCI) systems. A large proportion of the existing EEG signal preprocessing methods focus on isolating single artifact per time from an ensemble of EEG trials and require calibration and/or reference electrodes, resulting in increased complexity of their application to MI-EEG controlled rehabilitation devices in practical settings. Also, a few existing multi-artifacts removal methods though explored in other domains, they have rarely been investigated in the space of MI-EEG signals for multiple artifacts cancellation in a simultaneous manner. APPROACH: Building on the premise of previous works, this study propose a semi-automatic EEG preprocessing method that combines Generalized Eigenvalue Decomposition driven by low-rank approximation and a Multi-channel Wiener Filter (GEVD-MWF) that employs a learning technique for simultaneous elimination of multiple artifacts from MI-EEG signals. The proposed method is applied to remove multiple artifacts from 64-channel EEG signals recorded from transhumeral amputees while they performed distinct classes of upper limb MI tasks before decoding their movement intent using a selection of features and machine learning algorithms. MAIN RESULTS: Experimental results show that the proposed GEVD-MWF method yields significant improvements in MI decoding accuracies, in the range of 13.23%-41.21% compared to four existing popular artifact removal algorithms. Further investigation revealed that the GEVD-MWF approach enabled accuracies in the range of 90.44% - 99.67% using "single trial" EEG recordings, which could eliminate the need to record and process large ensembles of EEG trials as commonly required in some existing approaches. Additionally, using a variant of the sequential forward floating selection algorithm, a subset of 9 channels was used to obtain a decoding accuracy of 93.73%±1.58%. SIGNIFICANCE: Given its improved performance, reduced data requirements, and feasibility with few channels, the proposed GEVD-MWF could potentially spur the development of effective real-time control strategies for multi-degree of freedom EEG-based miniaturized rehabilitation robotic interfaces.

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions. METHODS: The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition. RESULTS: The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space. CONCLUSION: This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

Towards effective assessment of normal hearing function from ABR using a time-variant sweep-tone stimulus approach.

Objective. The auditory brainstem response (ABR) audiometry is a means of assessing the functional status of the auditory neural pathway in the clinic. The conventional click ABR test lacks good neural synchrony and it mainly evaluates high-frequency hearing while the common tone-burst ABR test only detects hearing loss of a certain frequency at a time. Additionally, the existing chirp stimuli are designed based on average data of cochlear characteristics, ignoring individual differences amongst subjects.Approach. Therefore, this study designed a new stimulus approach based on a sweep-tone concept with a time variant and spectrum characteristics that could be customized based on an individual's cochlear characteristics. To validate the efficiency of the proposed method, we compared its performance with the click and tone-bursts using ABR recordings from 11 normal-hearing adults.Main results. Experimental results showed that the proposed sweep-tone ABR achieved a higher amplitude compared with those elicited by the click and tone-bursts. When the stimulus level or rate was varied, the sweep-tone ABR consistently elicited a larger response than the corresponding click ABR. Moreover, the sweep-tone ABR appeared earlier than the click ABR under the same conditions. Specifically, the mean wave V peak-to-peak amplitude of the sweep-tone ABR was 1.3 times that of the click ABR at 70 dB nHL (normal hearing level) and a rate of 20 s-1, in which the former saved 40% of test time.Significance. In summary, the proposed sweep-tone approach is found to be more efficient than the traditional click and tone-burst in eliciting ABR.

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography.

Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes.Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0-9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations.Main Results.The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy.Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.

Towards image-based cancer cell lines authentication using deep neural networks.

Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.