Simultaneous Signal Acquisition Research Articles

Simultaneous acquisition of current and lateral force signals during AFM for characterising the piezoelectric and triboelectric effects of ZnO nanorods

Atomic force microscopy (AFM) is central to investigating the piezoelectric potentials of one-dimensional nanomaterials. The AFM probe is used to deflect individual piezoelectric nanorods and to measure the resultant current. However, the torsion data of AFM probes have not been exploited to elucidate the relationship between the applied mechanical force and resultant current. In this study, the effect of the size of ZnO nanorods on the efficiency of conversion of the applied mechanical force into current was investigated by simultaneously acquiring the conductive AFM and lateral force microscopy signals. The conversion efficiency was calculated based on linear regression analysis of the scatter plot of the data. This method is suitable for determining the conversion efficiencies of all types of freestanding piezoelectric nanomaterials grown under different conditions. A pixel-wise comparison of the current and lateral force images elucidated the mechanism of current generation from dense arrays of ZnO nanorods. The current signals generated from the ZnO nanorods by the AFM probe originated from the piezoelectric and triboelectric effects. The current signals contributed by the triboelectric effect were alleviated by using an AFM probe with a smaller spring constant and reducing the normal force.

Estimating the Effects of Awareness on Neck-Muscle Loading in Frontal Impacts with EMG and MC Sensors.

Critical traffic situations, such as vehicle collisions and emergency manoeuvres, can cause an occupant to respond with reflex and voluntary actions. These affect the occupant’s position and dynamic loading during interactions with the vehicle’s restraints, possibly compromising their protective function. Electromyography (EMG) is a commonly used method for measuring active muscle response and can also provide input parameters for computer simulations with models of the human body. The recently introduced muscle-contraction (MC) sensor is a wearable device with a piezo-resistive element for measuring the force of an indenting tip pressing against the surface of the body. The study aimed to compare how data collected simultaneously with EMG, video motion capture, and the novel MC sensor are related to neck-muscle loading. Sled tests with low-severity frontal impacts were conducted, assuming two different awareness conditions for seated volunteers. The activity of the upper trapezius muscle was measured using surface EMG and MC sensors. The neck-muscle load F was estimated from an inverse dynamics analysis of the head’s motion captured in the sagittal plane. The volunteers’ response to impact was predominantly reflexive, with significantly shorter onset latencies and more bracing observed when the volunteers were aware of the impact. Cross-correlations between the EMG and MC, EMG and F, and F and MC data were not changed significantly by the awareness conditions. The MC signal was strongly correlated (r = 0.89) with the neck-muscle loading F in the aware and unaware conditions, while the mean ΔF-MC delays were 21.0 ± 15.1 ms and 14.6 ± 12.4 ms, respectively. With the MC sensor enabling a consistent measurement-based estimation of the muscle loading, the simultaneous acquisition of EMG and MC signals improves the assessment of the reflex and voluntary responses of a vehicle’s occupant subjected to low-severity loading.

Experimental and Model-Based Study of the Vibrations in the Load Cell Response of Automatic Weight Fillers

The paper presents a study of the vibrations in the load cell response of automatic weight fillers for fluids, due to the dynamics of the system. The aim is to characterize vibratory phenomena through both experimental and model-based analysis, in order to identify the main causes and identify compensation strategies. Two test campaigns were conducted, on a test bench and on a sixteen stations machine, with the simultaneous acquisition of acceleration signals and load cell signals. A detailed sensitivity analysis based on experimental data, as many system parameters vary, has been developed. For the system modelling, a one Degree of Freedom (1 DoF) model, with lumped parameters and time-variant mass, including fluidic forces, was considered and numerically implemented. Genetic algorithms were used for the identification problems in the model-based analysis. The model allowed a deeper understanding of the phenomena that occur, showing promising results for the vibration prediction in a compensation process.

Design and development of a digital stethoscope encapsulation for simultaneous acquisition of phonocardiography and electrocardiography signals: the SmartHeart case study

The stethoscope is a major symbol of modern medicine. It is used for diagnosis of different conditions and enables physicians to listen to internal body sounds. Electrocardiography was only introduced in medicine in the beginning of the twentieth century. Today measuring heart’s electrical activity is also fundamental cardiac diseases diagnosis. Although performed with independent devices, requiring physician and patient presence in the same physical space, in combination they enhance cardiovascular assessment. In this paper, a digital stethoscope encapsulation was designed, adding new functionality to this advanced medical device. Today wired and wireless communications enable different medical devices to share data and information, over long distances. Using low-cost hardware technologies, the encapsulation will add the ability to acquire and transmit via Bluetooth the Electrocardiographic activity, determined in the same cardiac focus and synchronised with the Phonocardiographic sound recordings. Several encapsulation concepts were developed and prototyped using 3D printing. They were easily fitted to the digital stethoscope and tested in a hospital environment for ergonomics, acoustic and electric signals acquisition. The best concept was chosen with the help of a physician’s opinion and the final prototype performance was very satisfactory.

AFM-in-SEM as a Tool for Comprehensive Sample Surface Analysis

Abstract

Parallel magnetic particle imaging.

Magnetic Particle Imaging (MPI) is a promising tomographic method to visualize the distribution of superparamagnetic materials in three-dimensions. For encoding, a strong gradient represented by a field free point (FFP) or a field free line (FFL) is steered rapidly through the field of view (FOV), acquiring the signal successively. Conventional MPI scanners only provide a single FFP or FFL to sample the entire scan volume, which limits the size of the FOV and/or the temporal resolution. The alternative scanner concept of Traveling Wave MPI (TWMPI) uses a dynamic linear gradient array (dLGA) for dynamic FFP generation along the symmetry axis. The TWMPI scanner is capable of creating multiple FFPs simultaneously, and usually care is taken to locate only a single FFP in the desired FOV. In this manuscript, the concept of parallel MPI utilizing multiple FFPs simultaneously is introduced. For that, conceptual simulations are presented followed by reconstruction approaches for visualization of parallel MPI signals. In addition, an initial parallel MPI experiment with simultaneous acquisition of signals from two FFPs inside the FOV of the same scanner using two receive chains is demonstrated. This allows scanning a doubled FOV within the same acquisition time without sacrificing resolution compared to the standard TWMPI scanner.

Photoelectrochemical and electrochemical ratiometric aptasensing: A case study of streptomycin

There has been much interest in constructing ratiometric sensors using different sensing techniques because of their synergistic effect, although the simultaneous collection of the signals is challenging. Herein, we propose a ratiometric aptasensing strategy based on the dual-detection model with a photoelectrochemical (PEC) “signal-on” and an electrochemical (EC) “signal-off”. As a proof-of-concept study, CdTe quantum dots (CdTe QDs) and a methylene blue-labeled aptamer (MB-Apt) were used to generate PEC and EC signals in the sensing system. The target-induced conformational change of MB-Apt pushed MB away from the electrode, thereby decreasing the EC signal; at the same time, the reduced steric hindrance favored the restoration of the PEC signal from the CdTe QDs. Thus, this PEC-EC strategy can achieve the PEC “signal-on” and EC “signal-off” states simultaneously, as well as allowing quantitative analysis of the target based on the ratio of the current intensities. As a model application, an aptasensor fabricated for streptomycin detection showed a wide linear range from 0.03 to 100 μM with a detection limit of 10 nM (S/N = 3). The proposed sensing platform displayed superior analytical properties compared with methods based on PEC or EC alone. Our work provides an efficient dual-detection model-based ratiometric strategy for advanced analysis, and paves the way to the simultaneous acquisition of signals.

Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG

In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator.

A Confocal Reflection Super-Resolution Technique to Image Golgi-Cox Stained Neurons.

Metal-based Golgi-Cox (GC) staining is an established method used to visualise neurons with great morphological detail. Although GC stained samples are imaged routinely under transmitted light microscopy, this method is unable to yield information on the three-dimensional structure of dendrites and neurons and thus help reveal the connective properties of the central nervous system. Although a few studies have attempted simultaneous visualisation of GC staining and antigen-specific fluorescent labelling under a confocal reflection technique, the resolution of both confocal reflection and fluorescence modalities used to acquire GC reflection and fluorescently stained antibody signals are still limited by the diffraction limit of light at about 220 nm. Here, we report a confocal reflection super-resolution technique (CRSR) to break this diffraction barrier, which is achieved by minimising the pinhole size from 1 airy unit (AU) to 0.1 AU. This is achieved by minimising or closing the confocal pinhole size and is possible in this reflection modality, unlike fluorescence, because it is not a photon limited technique. Utilising the lowest wavelength of light available in the system (405 nm), the CRSR technique results in ∼30% lateral and axial resolution improvement. We also show that the CRSR technique can be used in conjunction to visualise both GC and immunofluorescence targets to create precise and improved three-dimensional visualisation and analysis. In addition, using these superresolution confocal reflection data sets from GC in CRSR mode significantly reduced the data overestimation, improving the accuracy of statistical analysis of dendritic spine density and average spine dimensions. Combining the 0.1 AU setting with deconvolution routines, the signal-to-noise ratio and resolution could further be improved an additional ∼20-25%, yielding CRSR images with resolutions up to 2-fold over the diffraction limit both laterally and axially. The improved precision of both visualisation and quantification of subdiffraction limited dendritic spines using the CRSR technique may prove to be critical in investigations that concern changes in detailed neuron morphology under central nervous system disease conditions such as multiple sclerosis and Alzheimer's disease. LAY DESCRIPTION: For over a century, Golgi-Cox (GC) has been a leading staining technique in the field of neuroscience, used to visualise neurons with great morphological detail. GC stained brain or spinal cord samples are conventionally visualised under transmitted light techniques. This limits the view of Golgi-staining to a two-dimensional image. A recent report showed that Golgi staining can be visualised in three-dimensions using the reflection modality of the confocal microscope. This visualisation also allows for the simultaneous acquisition of immunofluorescence signals. However, the reported resolution of Golgi staining confocal reflection is limited by the diffraction limit of light, which is around 220 nm. Here, we report a superresolution confocal reflection technique (CRSR) that achieves superresolution by minimising the pinhole size used in confocal microscopy. The CRSR technique results in ∼30% lateral and axial resolution improvement. Adding a deconvolution step in the final processing could improve the SNR and resolution even further up to 2-fold improvement in resolution over the diffraction limit both laterally and axially. We hope that this improved visualisation will help in investigations that concern changes in detailed neuron morphology under central nervous system disease conditions such as multiple sclerosis and Alzheimer's disease.

Toward Portable Hybrid Surface Electromyography/A-Mode Ultrasound Sensing for Human–Machine Interface

It is evident that non-invasive muscle-based human–machine interface (HMI) has been the research focus of human–machine interaction. To improve the performance of muscle-based HMI, it is significantly important to obtain electrophysiological and morphological changes of muscle contraction. However, there is still lacking of solution to present electrophysiological and morphological information of the same muscle at the same time. Surface electromyography (sEMG) can reflect the electrical activity of functional muscle contraction and A-mode ultrasound (AUS) can monitor the morphological structure of active muscle, both in non-invasive manners. This paper proposes a portable hybrid sEMG/AUS system for HMI. The system consists of composite sensor armband and signal acquisition modules, where the former achieves arrangement of two kinds of sensors at the same muscle position and the latter enables the simultaneous acquisition of sEMG and AUS signals. The hardware evaluation experiment proves that the system can provide high-quality signals in respect to signal-to-noise ratio (SNR) and time–frequency characteristics. Furthermore, the hand gesture recognition experiment validates the complementarity between sEMG-based and AUS-based HMI, since the recognition accuracy of hybrid sEMG/AUS feature is significantly improved by 4.85% ( ${p}$ = 0.0095) and 20.6% ( ${p} ) compared to the results of ultrasound features and sEMG features, respectively.

An Energy Efficient Multi-User Asynchronous Wireless Transmitter for Biomedical Signal Acquisition.

The paper presents a novel transmitter architecture for short-range asynchronous wireless communication, applicable to simultaneous multi-user wireless acquisition of biological signals. The analog signal, provided from an analog biosensor, is transformed to time information using an Integral Pulse Frequency Modulator (IPFM) as a Time-Encoding Machine. The IPFM generates a time-encoded unipolar pulse train, maintaining the linear dependence of the output pulse distance on analog input voltage. The system enables continuous acquisition of the signals from multiple sensors in which each transmitter has unique feedback loop delay used for multi-user coding. IPFM pulses trigger the Impulse Radio Ultra-Wideband pulse generator directly, providing two ultra-wideband (UWB) pulses per each IPFM pulse. Due to the lack of internal clock signal and microprocessor-free multi-user coding, the circuitry satisfies the requirements of multi-user coding energy efficiency and size reduction, which are crucial demands in biomedical applications. The proposed Time-Encoded UWB (TE-UWB) transmitter is implemented in 0.18 [Formula: see text] CMOS technology. Measurement results of the IPFM transfer function for input voltage ranging from 0.15 to 1.5 V are presented, providing the dependence of the IPFM pulse time distance on analog input voltage and power consumption dependence on the input voltage level. For continuous monitoring operation, total power consumption of the transmitter circuitry for the maximum input voltage is 10.8 [Formula: see text], while for the lowest input voltage it increases to 40.48[Formula: see text]. The circuit occupies 0.14 [Formula: see text].

A posteriori synchronization of scanning transmission electron microscopy signals with kilopixel per second acquisition rates

The stability and sensitivity of scanning transmission electron microscopes as well as detectors collecting e.g. electrons which suffered different scattering processes, or secondary radiation, have increased tremendously during the last decade. In order to fully exploit capabilities of simultaneously recording various signals with up to 1000 px/s acquisition rates the central issue is their synchronization. The latter is frequently a non-trivial problem without commercially available solution especially if detectors of different manufacturers are involved. In this paper, we present a simple scanning pattern enabling a posteriori synchronization of arbitrarily many signals being recorded entirely independently. We apply the approach to the simultaneous atomic-scale acquisition of signals from an annular dark-field detector and electron energy loss as well as energy-dispersive x-ray spectrometers. Errors emerging in scanning direction due to the independence of the respective processes are quantified and found to have a standard deviation of roughly half the pixel spacing. Since there are no intermediate waiting periods to maintain synchronicity, the proposed acquisition process is, in fact, demonstrated to be 12% faster than a commercial hardware-synchronized solution for identical sub-millisecond signal integration times and hence follows the trend in electron microscopy to extract more information per irradiating electron.

Comparison of EMG power during sleep from the submental and frontalis muscles.

ObjectiveSubmentalis electromyography (sEMG) and frontalis electromyography (fEMG) muscle activities have been used to assist in the staging of sleep and detection of disruptions in sleep. This study was designed to assess the concordance between sEMG and fEMG power, by and across sleep stages.MethodsForty-three records with simultaneous acquisition of differential signals from the submental and frontalis muscles were evaluated. Sleep stages were assigned using the poly-somnography signals based on majority agreement of five technicians. The sEMG and fEMG signals were identically filtered and aligned prior to cross-correlation analysis.ResultsA strong concordance between sEMG and fEMG power was observed, with 95% of the records exhibiting at least moderate agreement. During rapid eye movement (REM) sleep, sEMG power was significantly less than fEMG power, but exhibited four times greater across-subject variability. fEMG power during wake and non-REM (NREM) sleep was greater than sEMG power, but with 50% less variability. Differences in wake and N1 mean power and between the other sleep stages were more distinct in the fEMG recordings. Relative changes in sEMG and fEMG power across wake, NREM, and REM stages were essentially identical with median by-subject cross correlations of 0.98 and interquartile ranges of 0.97 and 0.99, respectively.ConclusionThe fEMG and sEMG power values were similar during wakefulness and sleep; however, the frontalis exhibits substantially less between-subject variability. This study established face validity for the use of fEMG in the detection of wake and stages of sleep, and for future applications toward assessment of quantitative REM sleep muscle activity in REM sleep behavior disorder.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

Universal simultaneous multiplex ELISA of small molecules in milk based on dual luciferases

The enzyme-linked immunosorbent assay (ELISA) has become the most important and widely used rapid detection technology for food safety because of its simple operation, fast speed and high sensitivity. Multiplex synchronous detection is the goal of ELISA that is always pursuing for. However, the reported multiplex ELISAs have not truly realized synchronous detection because of the complex signal generation and collection procedures. Here, we developed a dual-luciferases competitive direct bioluminescent immunoassay (DBL-cdELISA) with only one substrate addition step followed immediately by simultaneous signal acquisition. It is the first report of simultaneous multiplex analysis of small molecules based on microtiter plates and enzymes without any additional steps. The IC50 values for norfloxacin (NOR) and sulfamethazine (SMZ) were 0.051 ng mL−1 and 0.211 ng mL−1, respectively. The results demonstrated that the application of different luciferases and substrates simplified the signal generation and collection procedures and enabled simultaneous detection of small molecules with a simple procedure, high throughput and fast speed, that will be of great significance for the development of multiple assays.

Epi-detecting label-free multimodal imaging platform using a compact diode-pumped femtosecond solid-state laser

We have developed an epi-detected multimodal nonlinear optical microscopy platform based on a compact and cost-effective laser source featuring simultaneous acquisition of signals arising from hyperspectral coherent anti-Stokes Raman scattering (CARS), two-photon fluorescence, and second harmonic generation. The laser source is based on an approach using a frequency-doubled distributed Bragg reflector-tapered diode laser to pump a femtosecond Ti:sapphire laser. The operational parameters of the laser source are set to the optimum trade-off between the spectral and temporal requirements for these three modalities, achieving sufficient spectral resolution for CARS in the lipid region. The experimental results on a biological tissue reveal that the combination of the epi-detection scheme and the use of a compact diode-pumped femtosecond solid-state laser in the nonlinear optical microscope is promising for biomedical applications in a clinical environment.

Observation and analysis of the sparkover around grounding electrode under impulse current

Soil sparkover is one of the key factors that affect the impulse characteristics of grounding devices under lightning current. In this study, the polyester-based sensitive films are applied to observe the morphology features of the sandy sparkover around a horizontal grounding rod 1 m in length buried in a hemispheric pool 8 m in diameter under impulse current. Moreover, the releasing current distribution of the grounding rod is measured by modular multi-channel simultaneous signal acquisition system. Along with injected impulse current increasing, the ratio of current released from two ends of the rod increases because the spark discharge leads the redistribution of the dissipating current. The discharge in the sand becomes more intense with current increasing. The discharge on middle segments spreads around the electrode-like cluster, while sparkover at the terminal develops into tree creeping discharge finally. Analysis based on combining observation images with current measurement result suggests that sparkover morphology is related to not only electric field value but also electric field distribution. The sparkover strength is determined by electric field intensity, while the sparkover concentration depends on electric field uniformity.

Comparison of 3 T and 7 T ASL techniques for concurrent functional perfusion and BOLD studies

Arterial spin labeling (ASL) is the primary non-invasive MRI approach to measure baseline cerebral blood flow (CBF) in healthy subjects and patients. ASL also allows concurrent functional BOLD signal and CBF measurements, but the latter typically suffer from low contrast-to-noise (CNR) ratio. Ultra-high-field imaging significantly boosts BOLD signal CNR. However, it is contested whether also CBF CNR benefits from increasing magnetic field strength, especially given that technical challenges related to field inhomogeneities and power deposition constraints exist. Recently, we presented an optimized PASL technique that utilizes tr-FOCI inversion pulses and dielectric pads to overcome the temporal resolution limitations of previous 7T ASL implementations (Ivanov et al., in press; 2017). The primary goal of this study was to compare its performance to that of 3T ASL approaches – both pulsed ASL (PASL) and pseudo-continuous (pCASL) – concerning functional studies using simultaneous CBF and BOLD signal acquisition. To this aim, we investigated a wide range of parameters that can influence CBF and BOLD signal sensitivities: spatial resolution, labeling scheme, parallel imaging and echo time. We found that 7T ASL is superior in terms of CBF and BOLD temporal signal-to-noise ratio (SNR) and activation volume compared to all 3T ASL variants, in particular at high spatial resolution. Our results show that the advantages of 7T for ASL stem from increased image SNR, especially when parallel imaging is used. The gray matter baseline CBF was in good agreement for all 3T ASL variants, but a significantly lower value was obtained at 7T. The labeling scheme utilized was also found to significantly influence the measured perfusion territories CBF. In conclusion, a single-echo accelerated 7T PASL is recommended for high spatial and temporal resolution CBF and BOLD imaging, while a 3T dual-echo pCASL approach without parallel imaging may be preferred for low (i.e., 3mm isotropic and lower) resolution functional perfusion and BOLD applications.

A Multimodal System for Assessing Alertness Levels Due to Cognitive Loading.

This paper proposes a scheme for assessing the alertness levels of an individual using simultaneous acquisition of multimodal physiological signals and fusing the information into a single metric for quantification of alertness. The system takes electroencephalogram, high-speed image sequence, and speech data as inputs. Certain parameters are computed from each of these measures as indicators of alertness and a metric is proposed using a fusion of the parameters for indicating alertness level of an individual at an instant. The scheme has been validated experimentally using standard neuropsychological tests, such as the Visual Response Test (VRT), Auditory Response Test (ART), a Letter Counting (LC) task, and the Stroop Test. The tests are used both as cognitive tasks to induce mental fatigue as well as tools to gauge the present degree of alertness of the subject. Correlation between the measures has been studied and the experimental variables have been statistically analyzed using measures such as multivariate linear regression and analysis of variance. Correspondence of trends obtained from biomarkers and neuropsychological measures validate the usability of the proposed metric.

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG.

Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.