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    A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural recording
    (IEEE, 2013-09-26) Jian, Xu; Islam, Md Kafiul; Wang, Shuo; Yang, Zhi
    Experiment analysis on in-vivo data sequences suggests a wide system dynamic range (DR) is required to simultaneously record local field potentials (LFPs), extra-cellular spikes, and artifacts/interferences. In this paper, we present a 13 μW 87 dB DR ΔΣ modulator for full-spectrum neural recording. To achieve a wide DR and low power consumption, a fully-differential topology is used with multi-bit (MB) quantization scheme and switched-opamp (SO) technique. By adopting a novel fully-clocked scheme, a power-efficient current-mirror SO is developed with 50% power saving, which doubles the figure-of-merit (FOM) over its counterpart. A new static power-less multi-bit quantizer with 96% power and 69% area reduction is also introduced. Besides, instead of metal-insulator-metal (MIM) capacitor, three high-density MOS capacitor (MOSCAP) structures are employed to reduce circuit area. Measurement results show a peak signal-to-noise and distortion ratio (SNDR) of 85 dB with 10 kHz bandwidth at 1.0 V supply, corresponding to an FOM of 45 fJ/conv.-step. which is implemented in a 0.18 μm CMOS.
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    Methods for Artifact Detection and Removal from Scalp EEG: A Review
    (Elsevier, 2016-10-15) Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi
    Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.
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    Artifact Characterization and Removal for In-Vivo Neural Recording
    (Elsevier, 2014-04-15) Islam, Md Kafiul; Rastegarnia, Amir; Nguyen, Anh Tuan; Yang, Zhi
    Background: In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest. New method: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed. Results: Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio. Comparison with existing methods: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms. Conclusion: Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments
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    A Wavelet-Based Artifact Reduction from Scalp EEG for Epileptic Seizure Detection
    (IEEE, 2015-07-15) Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi
    This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.
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    A High Performance Delta-Sigma Modulator for Neurosensing
    (Sensors, 2015-08-07) Jian, Xu; Zhao, Menglian; Wu, Xiaobo; Islam, Md Kafiul; Yang, Zhi
    Recorded neural data are frequently corrupted by large amplitude artifacts that are triggered by a variety of sources, such as subject movements, organ motions, electromagnetic interferences and discharges at the electrode surface. To prevent the system from saturating and the electronics from malfunctioning due to these large artifacts, a wide dynamic range for data acquisition is demanded, which is quite challenging to achieve and would require excessive circuit area and power for implementation. In this paper, we present a high performance Delta-Sigma modulator along with several design techniques and enabling blocks to reduce circuit area and power. The modulator was fabricated in a 0.18-μm CMOS process. Powered by a 1.0-V supply, the chip can achieve an 85-dB peak signal-to-noise-and-distortion ratio (SNDR) and an 87-dB dynamic range when integrated over a 10-kHz bandwidth. The total power consumption of the modulator is 13 μW, which corresponds to a figure-of-merit (FOM) of 45 fJ/conversion step. These competitive circuit specifications make this design a good candidate for building high precision neurosensors.