Development of artificial intelligence-driven biosignal-sensitive cardiopulmonary resuscitation robot.

AIM OF THE STUDY: We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes. METHODS: We developed an AI-driven CPR robot which utilizes an integrated feedback system with an AI model predicting carotid blood flow (CBF). Twelve pigs were assigned to the AI robot group (n = 6) and the LUCAS 3 group (n = 6). They underwent 6 min of CPR after 7 min of ventricular fibrillation. In the AI robot group, the robot explored for the optimal compression position, depth and rate during the first 270-second period, and continued CPR with the optimal setup during the next 90-second period and beyond. The primary outcome was CBF during the last 90-second period. The secondary outcomes were coronary perfusion pressure (CPP), end-tidal carbon dioxide level (ETCO2) and return of spontaneous circulation (ROSC). RESULTS: The AI model's prediction performance was excellent (Pearson correlation coefficient = 0.98). CBF did not differ between the two groups [estimate and standard error (SE), -23.210 ± 20.193, P = 0.250]. CPP, ETCO2 level and rate of ROSC also did not show difference [estimate and SE, -0.214 ± 7.245, P = 0.976 for CPP; estimate and SE, 1.745 ± 3.199, P = 0.585 for ETCO2; 5/6 (83.3%) vs. 4/6 (66.7%), P = 1.000 for ROSC). CONCLUSION: This study provides proof of concept that an AI-driven CPR robot in porcine cardiac arrest is feasible. Compared to a LUCAS 3, an AI-driven CPR robot produced comparable hemodynamic and clinical outcomes.

Early Eye Disengagement Is Regulated by Task Complexity and Task Repetition in Visual Tracking Task.

Understanding human actions often requires in-depth detection and interpretation of bio-signals. Early eye disengagement from the target (EEDT) represents a significant eye behavior that involves the proactive disengagement of the gazes from the target to gather information on the anticipated pathway, thereby enabling rapid reactions to the environment. It remains unknown how task difficulty and task repetition affect EEDT. We aim to provide direct evidence of how these factors influence EEDT. We developed a visual tracking task in which participants viewed arrow movement videos while their eye movements were tracked. The task complexity was increased by increasing movement steps. Every movement pattern was performed twice to assess the effect of repetition on eye movement. Participants were required to recall the movement patterns for recall accuracy evaluation and complete cognitive load assessment. EEDT was quantified by the fixation duration and frequency within the areas of eye before arrow. When task difficulty increased, we found the recall accuracy score decreased, the cognitive load increased, and EEDT decreased significantly. The EEDT was higher in the second trial, but significance only existed in tasks with lower complexity. EEDT was positively correlated with recall accuracy and negatively correlated with cognitive load. Performing EEDT was reduced by task complexity and increased by task repetition. EEDT may be a promising sensory measure for assessing task performance and cognitive load and can be used for the future development of eye-tracking-based sensors.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

Pupil Response in Visual Tracking Tasks: The Impacts of Task Load, Familiarity, and Gaze Position.

Pupil size is a significant biosignal for human behavior monitoring and can reveal much underlying information. This study explored the effects of task load, task familiarity, and gaze position on pupil response during learning a visual tracking task. We hypothesized that pupil size would increase with task load, up to a certain level before decreasing, decrease with task familiarity, and increase more when focusing on areas preceding the target than other areas. Fifteen participants were recruited for an arrow tracking learning task with incremental task load. Pupil size data were collected using a Tobii Pro Nano eye tracker. A 2 × 3 × 5 three-way factorial repeated measures ANOVA was conducted using R (version 4.2.1) to evaluate the main and interactive effects of key variables on adjusted pupil size. The association between individuals' cognitive load, assessed by NASA-TLX, and pupil size was further analyzed using a linear mixed-effect model. We found that task repetition resulted in a reduction in pupil size; however, this effect was found to diminish as the task load increased. The main effect of task load approached statistical significance, but different trends were observed in trial 1 and trial 2. No significant difference in pupil size was detected among the three gaze positions. The relationship between pupil size and cognitive load overall followed an inverted U curve. Our study showed how pupil size changes as a function of task load, task familiarity, and gaze scanning. This finding provides sensory evidence that could improve educational outcomes.

Biosignal comparison for autism assessment using machine learning models and virtual reality.

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction.

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

A Generative Method for a Laryngeal Biosignal.

The Glottal Area Waveform (GAW) is an important component in quantitative clinical voice assessment, providing valuable insights into vocal fold function. In this study, we introduce a novel method employing Variational Autoencoders (VAEs) to generate synthetic GAWs. Our approach enables the creation of synthetic GAWs that closely replicate real-world data, offering a versatile tool for researchers and clinicians. We elucidate the process of manipulating the VAE latent space using the Glottal Opening Vector (GlOVe). The GlOVe allows precise control over the synthetic closure and opening of the vocal folds. By utilizing the GlOVe, we generate synthetic laryngeal biosignals. These biosignals accurately reflect vocal fold behavior, allowing for the emulation of realistic glottal opening changes. This manipulation extends to the introduction of arbitrary oscillations in the vocal folds, closely resembling real vocal fold oscillations. The range of factor coefficient values enables the generation of diverse biosignals with varying frequencies and amplitudes. Our results demonstrate that this approach yields highly accurate laryngeal biosignals, with the Normalized Mean Absolute Error values for various frequencies ranging from 9.6 â‹… 10-3 to 1.20 â‹… 10-2 for different experimented frequencies, alongside a remarkable training effectiveness, reflected in reductions of up to approximately 89.52% in key loss components. This proposed method may have implications for downstream speech synthesis and phonetics research, offering the potential for advanced and natural-sounding speech technologies.

Arterial Pulse Wave Velocity Signal Reconstruction Using Low Sampling Rates.

Pulse Wave Velocity (PWV) analysis is valuable for assessing arterial stiffness and cardiovascular health and potentially for estimating blood pressure cufflessly. However, conventional PWV analysis from two transducers spaced closely poses challenges in data management, battery life, and developing the device for continuous real-time applications together along an artery, which typically need data to be recorded at high sampling rates. Specifically, although a pulse signal consists of low-frequency components when used for applications such as determining heart rate, the pulse transit time for transducers near each other along an artery takes place in the millisecond range, typically needing a high sampling rate. To overcome this issue, in this study, we present a novel approach that leverages the Nyquist-Shannon sampling theorem and reconstruction techniques for signals produced by bioimpedance transducers closely spaced along a radial artery. Specifically, we recorded bioimpedance artery pulse signals at a low sampling rate, reducing the data size and subsequently algorithmically reconstructing these signals at a higher sampling rate. We were able to retain vital transit time information and achieved enhanced precision that is comparable to the traditional high-rate sampling method. Our research demonstrates the viability of the algorithmic method for enabling PWV analysis from low-sampling-rate data, overcoming the constraints of conventional approaches. This technique has the potential to contribute to the development of cardiovascular health monitoring and diagnosis using closely spaced wearable devices for real-time and low-resource PWV assessment, enhancing patient care and cardiovascular disease management.

Early detection of late-onset neonatal sepsis from noninvasive biosignals using deep learning: A multicenter prospective development and validation study.

BACKGROUND: Neonatal sepsis is responsible for significant morbidity and mortality worldwide. Its accurate and timely diagnosis is hindered by vague symptoms and the urgent necessity for early antibiotic intervention. The gold standard for diagnosing the condition is the identification of a pathogenic organism from normally sterile sites via laboratory testing. However, this method is resource-intensive and cannot be conducted continuously. OBJECTIVE: This study aimed to predict the onset of late-onset sepsis (LOS) with good diagnostic value as early as possible using non-invasive biosignal measurements from neonatal intensive care unit (NICU) monitors. METHODS: In this prospective multicenter study, we developed a multimodal machine learning algorithm based on a convolutional neural network (CNN) structure that uses the power spectral density (PSD) of recorded biosignals to predict the onset of LOS. This approach aimed to discern LOS-related pathogenic spectral signatures without labor-intensive manual artifact removal. RESULTS: The model achieved an area under the receiver operating characteristic score of 0.810 (95 % CI 0.698-0.922) on the validation dataset. With an optimal operating point, LOS detection had 83 % sensitivity and 73 % specificity. The median early detection was 44 h before clinical suspicion. The results highlighted the additive importance of electrocardiogram and respiratory impedance (RESP) signals in improving predictive accuracy. According to a more detailed analysis, the predictive power arose from the morphology of the electrocardiogram's R-wave and sudden changes in the RESP signal. CONCLUSION: Raw biosignals from NICU monitors, in conjunction with PSD transformation, as input to the CNN, can provide state-of-the-art prediction performance for LOS without the need for artifact removal. To the knowledge of the authors, this is the first study to highlight the independent and additive predictive potential of electrocardiogram R-wave morphology and concurrent, sudden changes in the RESP waveform in predicting the onset of LOS using non-invasive biosignals.

A Path towards Timely VAP Diagnosis: Proof-of-Concept Study on Pyocyanin Sensing with Cu-Mg Doped Graphene Oxide.

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO (GO - Cu - Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.

Welded Carbon Nanotube-Graphene Hybrids with Tunable Strain Sensing Behavior for Wide-Range Bio-Signal Monitoring.

Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to prepare welded CNT-graphene hybrids with tunable compositions and a tunable bonding strength between components by the in situ reduction of CNT-graphene oxide (GO) hybrid by thermal annealing. The stiffness of the hybrid film could be tailored by both initial CNT/GO dosage and annealing temperature, through which its electromechanical behaviors could also be defined. The strain sensor based on the CNT-graphene hybrid could be applied to collect epidermal bio-signals by both capturing the faint skin deformation from wrist pulse and recording the large deformations from joint bending, which has great potential in health monitoring, motion sensing and human-machine interfacing.

[A novel method for detecting circulating tumor cells immunity based on micro-nano technique].

This study was to develop a new method for detecting circulating tumor cells (CTCs) with high sensitivity and specificity, therefore to detect the colorectal cancer as early as possible for improving the detection rate of the disease. To this end, we prepared some micro-column structure microchips modified with graphite oxide-streptavidin (GO-SA) on the surface of microchips, further coupled with a broad-spectrum primary antibody (antibody1, Ab1), anti-epithelial cell adhesion molecule (anti-EpCAM) monoclonal antibody to capture CTCs. Besides, carboxylated multi-walled carbon nanotubes (MWCNTs-COOH) were coupled with colorectal cancer related antibody as specific antibody 2 (Ab2) to prepare complex. The sandwich structure consisting of Ab1-CTCs-Ab2 was constructed by the microchip for capturing CTCs. And the electrochemical workstation was used to detect and verify its high sensitivity and specificity. Results showed that the combination of immunosensor and micro-nano technology has greatly improved the detection sensitivity and specificity of the immunosensor. And we also verified the feasibility of the immunosensor for clinical blood sample detection, and successfully recognitized detection and quantization of CTCs in peripheral blood of colorectal cancer patients by this immunosensor. In conclusion, the super sandwich immunosensor based on micro-nano technology provides a new way for the detection of CTCs, which has potential application value in clinical diagnosis and real-time monitoring of disease.

Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative.

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.

Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm.

BACKGROUND: Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature of the algorithm coupled with the hardware makes it challenging to understand the underlying mechanisms of the algorithms and integrate them with other monitoring systems, thereby limiting their use. OBJECTIVE: We propose an interpretable deep learning model that forecasts BIS values 25 s in advance using 30 s electroencephalogram (EEG) data. MATERIAL AND METHODS: The proposed model utilized EEG data as a predictor, which is then decomposed into amplitude and phase components using fast Fourier Transform. An attention mechanism was applied to interpret the importance of these components in predicting BIS. The predictability of the model was evaluated on both regression and binary classification tasks, where the former involved predicting a continuous BIS value, and the latter involved classifying a dichotomous status at a BIS value of 60. To evaluate the interpretability of the model, we analyzed the attention values expressed in the amplitude and phase components according to five ranges of BIS values. The proposed model was trained and evaluated using datasets collected from two separate medical institutions. RESULTS AND CONCLUSION: The proposed model achieved excellent performance on both the internal and external validation datasets. The model achieved a root-mean-square error of 6.614 for the regression task, and an area under the receiver operating characteristic curve of 0.937 for the binary classification task. Interpretability analysis provided insight into the relationship between EEG frequency components and BIS values. Specifically, the attention mechanism revealed that higher BIS values were associated with increased amplitude attention values in high-frequency bands and increased phase attention values in various frequency bands. This finding is expected to facilitate a more profound understanding of the BIS prediction mechanism, thereby contributing to the advancement of anesthesia technologies.

Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning.

Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.

Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction.

Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM model based on multitask training and attention mechanism for IOH prediction. We trained and tested our proposed model using bio-signal waveforms obtained from patient monitoring of non-cardiac surgery. We selected three models (WaveNet, CNN, and TCN) that process time-series data for comparison. The experimental results demonstrate that our proposed model has optimal MSE (43.83) and accuracy (0.9224) compared to other models, including WaveNet (51.52, 0.9087), CNN (318.52, 0.5861), and TCN (62.31, 0.9045), which suggests that our proposed model has better regression and classification performance. We conducted ablation experiments on the multitask and attention mechanisms, and the experimental results demonstrated that the multitask and attention mechanisms improved MSE and accuracy. The results demonstrate the effectiveness and superiority of our proposed model in predicting IOH.

Accelerometer-based detection of African swine fever infection in wild boar.

Infectious wildlife diseases that circulate at the interface with domestic animals pose significant threats worldwide and require early detection and warning. Although animal tracking technologies are used to discern behavioural changes, they are rarely used to monitor wildlife diseases. Common disease-induced behavioural changes include reduced activity and lethargy ('sickness behaviour'). Here, we investigated whether accelerometer sensors could detect the onset of African swine fever (ASF), a viral infection that induces high mortality in suids for which no vaccine is currently available. Taking advantage of an experiment designed to test an oral ASF vaccine, we equipped 12 wild boars with an accelerometer tag and quantified how ASF affects their activity pattern and behavioural fingerprint, using overall dynamic body acceleration. Wild boars showed a daily reduction in activity of 10-20% from the healthy to the viremia phase. Using change point statistics and comparing healthy individuals living in semi-free and free-ranging conditions, we show how the onset of disease-induced sickness can be detected and how such early detection could work in natural settings. Timely detection of infection in animals is crucial for disease surveillance and control, and accelerometer technology on sentinel animals provides a viable complementary tool to existing disease management approaches.

Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy.

BACKGROUND AND OBJECTIVE: Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants. METHODS: In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, ß, and photon count rate. RESULTS: Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively. CONCLUSION: This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.

Characterization of High-Gamma Activity in Electrocorticographic Signals.

Introduction: Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information. Methods: To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA. Results: The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks. Discussion: This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.