Bashir I. Morshed

Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA

Brain activities commonly recorded using the electroencephalogram (EEG) are contaminated with ocular artifacts. These activities can be suppressed using a robust independent component analysis (ICA) tool, but its efficiency relies on manual intervention to accurately identify the independent artifactual components. In this paper, we present a new unsupervised, robust, and computationally fast statistical algorithm that uses modified multiscale sample entropy (mMSE) and Kurtosis to automatically identify the independent eye blink artifactual components, and subsequently denoise these components using biorthogonal wavelet decomposition. A 95% two-sided confidence interval of the mean is used to determine the threshold for Kurtosis and mMSE to identify the blink related components in the ICA decomposed data. The algorithm preserves the persistent neural activity in the independent components and removes only the artifactual activity. Results have shown improved performance in the reconstructed EEG signals using the proposed unsupervised algorithm in terms of mutual information, correlation coefficient, and spectral coherence in comparison with conventional zeroing-ICA and wavelet enhanced ICA artifact removal techniques. The algorithm achieves an average sensitivity of 90% and an average specificity of 98%, with average execution time for the datasets (N = 7) of 0.06 s (SD = 0.021) compared to the conventional wICA requiring 0.1078 s (SD = 0.004). The proposed algorithm neither requires manual identification for artifactual components nor additional electrooculographic channel. The algorithm was tested for 12 channels, but might be useful for dense EEG systems.

Dielectrophoretic responses of DNA and fluorophore in physiological solution by impedimetric characterization

Characterization of the DNAs dielectrophoretic (DEP) behavior is the foundation of DNA manipulation by electric fields. This paper presents a label-free DNA differentiation technique by a combination of DEP response and impedimetric measurement on the microchip. In contrast to most of the recent studies on DEP manipulation of DNA that use deionized water or diluted DNA buffer where living biomolecules cannot survive, we used physiological solutions (PBS with 154 mM Na+) that are highly practical for pursuing DNA-based physical applications. The microchip, a commercial surface acoustic wave resonator, contains an array of interdigitated aluminum electrodes (1.4 μm width, 1.1 μm gap) on quartz substrate for DEP trap. Measurements were taken with a high precision impedance analyzer, which also acted as the excitation source to induce DEP response at 20 kHz, 50 kHz, 100 kHz, 300 kHz, 500 kHz, 1 MHz, 2 MHz and 5 MHz (N=3). To verify DEP response, fluorescence microscope images were captured before and after the electric excitation. Test results from the DEP experimentation after comparing with fluorescent images of pUC18 DNA show that a large change in impedance corresponds to positive DEP while little change corresponds to negative DEP. The strongest p-DEP and the maximum collection efficiency were observed around 300 kHz for supercoiled pUC18 and 100 kHz for linear λDNA. This work yields real-time impedimetric DEP response of DNA of different molecular conformations in practical settings. The technique can serve as the basis for submicron particle separation, disease diagnosis, cell life-circle research, and other applications in physiological surroundings.

Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data

Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.

Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG

For biomedical and scientific fields, Electroencephalography (EEG) has turned out to be an important tool to understand, study, and utilize brain functionalities. To fully utilize EEG signals in real-life closed-loop applications, artifacts such as ocular must be removed. Wavelet transform is one of the powerful methods to remove ocular artifacts from single channel EEG devices. In this study, both stationary and discrete wavelet transforms (SWT and DWT, respectively) have been compared with various wavelet basis functions, such as sym3, haar, coif3, and bior4.4 using either universal threshold (UT) or statistical threshold (ST). Different combinations of wavelet transform techniques, mother wavelets, and thresholds are compared to identify an optimum combination for ocular artifact removal. Performance metrics like Correlation Coefficient (CC), Normalized Mean Square Error (NMSE), Time Frequency Analysis, and execution time have been calculated for measuring the effectiveness of each combination. According to CC, DWT+UT combination turned out to be a good option for the ocular artifact removal. However, according to NMSE and time frequency analysis, SWT+ST has generated better performance in keeping neural segments of EEG unaffected. According to the measurement of execution times, DWT+ST is faster compared to other combinations. The study shows that wavelet transform is suitable in artifact removal from single channel EEG data to implement in ambulatory real-time EEG systems.

Real-time hybrid ocular artifact detection and removal for single channel EEG

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. However, EEG signals are usually contaminated by Ocular Artifacts (OA) such as eye blink activities. Removal of OA is critical to obtain clean EEG signals required for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent. Such ambulatory devices require real-time signal processing for immediate feedback. This paper presents a hybrid algorithm to detect and remove OA from single channel EEG signal using NeuroMonitor hardware platform. The algorithm first detects the eye blinks (OA zone) using Algebraic approach, and then removes artifact from OA zone using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone to keep the critical neural information intact. The OA removal algorithm is applied to the online data for 0.5 sec epoch length. The performance evaluation is carried out qualitatively and quantitatively using time-frequency analysis, mean square coherence and other statistical parameters, i.e. Correlation Coefficient and Mutual Information. Processing time for DWT was significantly lower (x25) to that of SWT. This proposed hybrid OA removal algorithm demonstrates real-time execution with sufficient accuracy.

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

NeuroMonitor ambulatory EEG device: Comparative analysis and its application for cognitive load assessment

We have previously presented a wireless ambulatory EEG device (NeuroMonitor) to non-invasively monitor prefrontal cortex scalp EEG activity in real-life settings. This paper discusses analysis and application of data acquired using this device. We assess the device data against a commercially available, clinical grade Neuroscan SynAmps RT EEG system. For the comparison, temporal statistical measures and Power Spectral Density (PSD) are computed for the simultaneous recordings from both devices from (nearly) identical electrode locations. Although the analog signal processing, sampling, and data recording specifications are slightly different for these devices (e.g., filter specifications, ADC - NeuroMonitor: 16 bit and Neuroscan: 24 bit, electrodes - NeuroMonitor: GS26 Pre-gelled Disposable, Neuroscan: Ag/AgCl reusable EEG disc electrodes), the temporal signals and the PSD of two devices had sufficient correlation. The paper also describes pilot data collection for a test protocol to determine cognitive load using the NeuroMonitor device. For analyzing attention levels for 5 different tasks, EEG rhythms (Alpha, Beta and Theta) are extracted and cognitive load index (CLI) is computed. Results show variations in the PSD of these rhythms with respect to corresponding expected cognitive loads in attention-related and relaxed tasks. This study validates the NeuroMonitor ambulatory EEG device data and shows a use-case for real-life cognitive load studies.

Ambulatory EEG NeuroMonitor platform for engagement studies of children with development delays

Engagement monitoring is crucial in many clinical and therapy applications such as early learning preschool classes for children with developmental delays including autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), or cerebral palsy; as it is challenging for the instructors to evaluate the individual responses of these children to determine the effectiveness of the teaching strategies due to the diverse and unique need of each child who might have difficulty in verbal or behavioral communication. This paper presents an ambulatory scalp electroencephalogram (EEG) NeuroMonitor platform to study brain engagement activities in natural settings. The developed platform is miniature (size: 2.2” x 0.8” x 0.36”, weight: 41.8 gm with 800 mAh Li-ion battery and 3 snap leads) and low-power (active mode: 32 mA low power mode: under 5mA) with 2 channels (Fp1, Fp2) to record prefrontal cortex activities of the subject in natural settings while concealed within a headband. The signals from the electrodes are amplified with a low-power instrumentation amplifier; notch filtered (fc = 60Hz), then band-passed by a 2nd-order Chebyshev-I low-pass filter cascaded with a 2nd-order low-pass (fc = 125Hz). A PSoC ADC (16-bit, 256 sps) samples this filtered signal, and can either transmit it through a Class-2 Bluetooth transceiver to a remote station for real-time analysis or store it in a microSD card for offline processing. This platform is currently being evaluated to capture data in the classroom settings for engagement monitoring of children, aimed to study the effectiveness of various teaching strategies that will allow the development of personalized classroom curriculum for children with developmental delays.

Body-worn fully-passive wireless analog sensors for physiological signal capture through load modulation using resistive transducers

Fully-passive wireless body-sensors pose viable solutions for unobtrusive monitoring of physiological signals at natural settings. While fully-passive capacitive analog passive wireless sensors has been reported, we present an alternative solution with resistive based transducers. The passive sensor is composed of a loop antenna, a tuning capacitor, and a resistive transducer suitable for the type of physiological signals to be measured. The scanner transmits carrier RF signal at 13.75MHz whose amplitude is modulated based on the resistive loading by the transducer. The load modulation is captured with the signal analyzer. The system was characterized for various resistive loads of 1.2Ω to 82KΩ and open at 5, 10, 20, and 40 mm co-axial distances between the transmitter and the receiver antennas. We demonstrate the practicality of the system by measuring several physiological signals like heart rate, temperature, and pulse oximetry. The wireless power used for remote sensing is very low (-20dBm, except pulse oximetry requires 0dBm). The results show the potential of developing a new set of body-worn fully-passive sensors for physiological signal monitoring.

Sample Entropy enhanced wavelet-ICA denoising technique for eye blink artifact removal from scalp EEG dataset

Scalp Electroencephalogram (EEG) recordings are usually contaminated with a variety of artifacts, which can be removed by threshold-based classifiers, Principal Component Analysis, Independent Component Analysis (ICA), wavelet-based multi-resolution analysis, or higher order statistics. In this paper we propose Sample Entropy, a self-sufficient statistical measure to identify the eye blink related independent components and Haar wavelet decomposition to subsequently denoise these components. The proposed method identified the blink artifactual components with an accuracy of 88% in our pilot study (N=4). The results demonstrated the improved performance of eye-blink artifacts removal with the neural activity intact in terms of Mutual Information (1.27 / 0.318 / 1.15), Correlation coefficient (0.574 / 0.369/ 0.569), and Standard deviation ratio (0.559/ 0.375 / 0.551) in comparison to standard Zeroing-ICA and wavelet-ICA based techniques, respectively. Instead of human expertise intervention to identify the eye blink components after Extended Infomax ICA decomposition, the algorithm offers potential for automation. This algorithm also offers advantage of being computationally fast and inexpensive, and does not require additional Electrooculographic signals for referencing.