A novel onset detection technique for brain?computer interfaces using sound-production related cognitive tasks in simulated-online system

Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies?Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset

Electromyography artefacts are a well-known problem in Electroencephalography studies (BCIs, brain mapping, and clinical areas). Blind source separation (BSS) techniques are commonly used to handle artefacts. However, these may remove not only EMG artefacts but also some useful EEG sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). Methods: The EMG-CCh are selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artefacts played a significant role in class separation. To ensure that promising results are not due to weak EMG removal, reliability tests were done. Results: In our data set, the comparison results between BSS artefact removal applied in two ways, to all channels and only to EMG-CCh, showed that ICA, PCA and BSS-CCA can yield significantly better (p<0.05) class separation with the proposed method (79% of the cases for ICA, 53% for PCA and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. Conclusion: The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. Significance: There are no existing methods for removing EMG artefacts based on the correlation between EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artefact handling methods. For these reasons, we believe this method can be useful for other EEG studies

Crexens™: an expandable general-purpose electrochemical analyzer

2019 Fall.Includes bibliographical references.Electrochemical analysis has gained a great deal of attention of late due to its low-cost, easy-to-perform, and easy-to-miniaturize, especially in personal health care where accuracy and mobility are key factors to bring diagnostics to patients. According to data from Centers for Medicare & Medicaid Services (CMS) in the US, the share of health expenditure in the US has been kept growing in the past 3 decades and reached 17.9% of its overall Gross Domestic Product till 2016, which is equivalent to $10,348 for every person in the US per year. On the other hand, health care resources are often limited not only in rural area but also appeared in well-developed countries. The urgent need and the lack of health resource brings to front the research interest of Point-of-Care (PoC) diagnosis devices. Electrochemical methods have been largely adopted by chemist and biologist for their research purposes. However, several issues exist within current commercial benchtop instruments for electrochemical measurement. First of all, the current commercial instruments are usually bulky and do not have handheld feature for point-of-care applications and the cost are easily near$5,000 each or above. Secondly, most of the instruments do not have good integration level that can perform different types of electrochemical measurements for different applications. The last but not the least, the existing generic benchtops instruments for electrochemical measurements have complex operational procedures that require users to have a sufficient biochemistry and electrochemistry background to operate them correctly. The proposed Crexens™ analyzer platform is aimed to present an affordable electrochemical analyzerwhile achieving comparable performance to the existing commercial instruments, thus, making general electrochemical measurement applications accessible to general public. In this dissertation, the overall Crexens™ electrochemical analyzer architecture and its evolution are presented. The foundation of the Crexens™ architecture was derived from two separate but related research in electrochemical sensing. One of them is a microelectrode sensor array using CMOS for neurotransmitter sensing; the other one is a DNA affinity-based capacitive sensor for infectious disease, such as ZIKA. The CMOS microelectrode sensor array achieved a 320uM sensitivity for norepinephrine, whereas the capacitive sensor achieved a dynamic range of detection from 1 /uL to 105 /uL target molecules (20 to 2 million targets), which makes it be within the detection range in a typical clinical application environment. This dissertation also covers the design details of the CMOS microelectrode array sensor and the capacitive sensor design as a prelude to the development of the Crexens™ analyzer architecture. Finally, an expandable integrated electrochemical analyzer architecture (Crexens™) has been designed for mobile point-of-care (POC) applications. Electrochemical methods have been explored in detecting various bio-molecules such as glucose, lactate, protein, DNA, neurotransmitter, steroid hormone, which resulted in good sensitivity and selectivity. The proposed system is capable of running electrochemical experiments including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), electrochemical capacitive spectroscopy (ECS), amperometry, potentiometry, and other derived electrochemical based tests. This system consist of a front-end interface to sensor electrodes, a back-end user interface on smart phone and PC, a base unit as master module, a low-noise add-on module, a high-speed add-on module, and a multi-channel add-on module. The architecture allows LEGO™-like capability to stack add-on modules on to the base-unit for performance enhancements in noise, speed or parallelism. The analyzer is capable of performing up to 1900 V/s CV with 10 mV step, up to 12 kHz EIS scan range and a limit of detection at 637 pA for amperometric applications with the base module. With high performance module, the EIS scan range can be extended upto 5 MHz. The limit of detection can be further improved to be at 333 fA using the low-noise module. The form factor of the electrochemical analyzer is designed for its mobile/point-of-care applications, integrating its entire functionality on to a 70 cm² area of surface space. A glutamine enzymatic sensor was used to valid the capability of the proposed electrochemical analyzer and turned out to give good linearity and reached a limit of detection at 50 uM

Sound-production Related Cognitive Tasks for Onset Detection in Self-Paced Brain-Computer Interfaces

Objective. The main goal of this research is proposing a novel method of onset detection for Self-Paced (SP) Brain-Computer Interfaces (BCIs) to increase usability and practicality of BCIs towards real-world uses from laboratory research settings. Approach. To achieve this goal, various Sound-Production Related Cognitive Tasks (SPRCTs) were tested against idle state in offline and simulated-online experiments. An online experiment was then conducted that turned a messenger dialogue on when a new message arrived by executing the Sound Imagery (SI) onset detection task in real-life scenarios (e.g. watching video, reading text). The SI task was chosen as an onset task because of its advantages over other tasks: 1) Intuitiveness. 2) Beneficial for people with motor disabilities. 3) No significant overlap with other common, spontaneous cognitive states becoming easier to use in daily-life situations. 4) No dependence on user’s mother language. Main results. The final online experimental results showed the new SI onset task had significantly better performance than the Motor Imagery (MI) approach. 84.04% (SI) vs 66.79% (MI) TFP score for sliding image scenario, 80.84% vs 61.07% for watching video task. Furthermore, the onset response speed showed the SI task being significantly faster than MI. In terms of usability, 75% of subjects answered SI was easier to use. Significance. The new SPRCT outperforms typical MI for SP onset detection BCIs (significantly better performance, faster onset response and easier usability), therefore it would be more easily used in daily-life situations. Another contribution of this thesis is a novel EMG artefact-contaminated EEG channel selection and handling method that showed significant class separation improvement against typical blind source separation techniques. A new performance evaluation metric for SP BCIs, called true-false positive score was also proposed as a standardised performance assessment method that considers idle period length, which was not considered in other typical metrics

Speech Emotion Recognition System using Librosa for Better Customer Experience

Call center employees usually depend on instinct to judge a potential customer and how to pitch to them. In this paper, we pitch a more effective way for call center employees to generate more leads and engagement to generate higher revenue by analyzing the speech of the target customer by using machine learning practices and depending on data to make data-driven decisions rather than intuition. Speech Emotion Recognition otherwise known as SER is the demonstration of aspiring to perceive human inclination along with the behavior. Normally voice reflects basic feeling through tone and pitch. According to human behavior, many creatures other than human beings are also synced themselves. In this paper, we have used a python-based library named Librosa for examining music tones and sounds or speeches. In this regard, various libraries are being assembled to build a detection model utilizing an MLP (Multilayer Perceptron) classifier. The classifier will train to perceive feeling from multiple sound records. The whole implementation will be based on an existing Kaggle dataset for speech recognition. The training set will be treated to train the perceptron whereas the test set will showcase the accuracy of the model

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

Vehicle Engine Classification Using of Laser Vibrometry Feature Extraction

Used as a non-invasive and remote sensor, the laser Doppler vibrometer (LDV) has been used in many different applications, such as inspection of aircrafts, bridge and structure and remote voice acquisition. However, using LDV as a vehicle surveillance device has not been feasible due to the lack of systematic investigations on its behavioral properties. In this thesis, the LDV data from different vehicles are examined and features are extracted. A tone-pitch indexing (TPI) scheme is developed to classify different vehicles by exploiting the engine’s periodic vibrations that are transferred throughout the vehicle’s body. Using the TPI with a two-layer feed-forward 20 intermediate-nodes neural network to classify vehicles’ engine, the results are encouraging as they can consistently achieve accuracies over 96%. However, the TPI required a length of 1.25 seconds of vibration, which is a drawback of the TPI, as vehicles generally are moving whence the 1.25 second signals are unavailable. Based on the success of TPI, a new normalized tone-pitch indexing (nTPI) scheme is further developed, using the engine’s periodic vibrations, and shortened the time period from 1.25 seconds to a reasonable 0.2 seconds. Keywords: LDV, Machine Learning, Neural network, Deep learning, Vehicle classificatio

On optimal design and applications of linear transforms

Linear transforms are encountered in many fields of applied science and engineering. In the past, conventional block transforms provided acceptable answers to different practical problems. But now, under increasing competitive pressures, with the growing reservoir of theory and a corresponding development of computing facilities, a real demand has been created for methods that systematically improve performance. As a result the past two decades have seen the explosive growth of a class of linear transform theory known as multiresolution signal decomposition. The goal of this work is to design and apply these advanced signal processing techniques to several different problems. The optimal design of subband filter banks is considered first. Several design examples are presented for M-band filter banks. Conventional design approaches are found to present problems when the number of constraints increases. A novel optimization method is proposed using a step-by-step design of a hierarchical subband tree. This method is shown to possess performance improvements in applications such as subband image coding. The subband tree structuring is then discussed and generalized algorithms are presented. Next, the attention is focused on the interference excision problem in direct sequence spread spectrum (DSSS) communications. The analytical and experimental performance of the DSSS receiver employing excision are presented. Different excision techniques are evaluated and ranked along with the proposed adaptive subband transform-based excises. The robustness of the considered methods is investigated for either time-localized or frequency-localized interferers. A domain switchable excision algorithm is also presented. Finally, sonic of the ideas associated with the interference excision problem are utilized in the spectral shaping of a particular biological signal, namely heart rate variability. The improvements for the spectral shaping process are shown for time-frequency analysis. In general, this dissertation demonstrates the proliferation of new tools for digital signal processing

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaPatients with neuromuscular and cardiorespiratory diseases need to be monitored continuously. This constant monitoring gives rise to huge amounts of multivariate data which need to be processed as soon as possible, so that their most relevant features can be extracted. The field of parallel processing, an area from the computational sciences, comes naturally as a way to provide an answer to this problem. For the parallel processing to succeed it is necessary to adapt the pre-existing signal processing algorithms to the modern architectures of computer systems with several processing units. In this work parallel processing techniques are applied to biosignals, connecting the area of computer science to the biomedical domain. Several considerations are made on how to design parallel algorithms for signal processing, following the data parallel paradigm. The emphasis is given to algorithm design, rather than the computing systems that execute these algorithms. Nonetheless, shared memory systems and distributed memory systems are mentioned in the present work. Two signal processing tools integrating some of the parallel programming concepts mentioned throughout this work were developed. These tools allow a fast and efficient analysis of long-term biosignals. The two kinds of analysis are focused on heart rate variability and breath frequency, and aim to the processing of electrocardiograms and respiratory signals, respectively. The proposed tools make use of the several processing units that most of the actual computers include in their architecture, giving the clinician a fast tool without him having to set up a system specifically meant to run parallel programs

Low-Noise Energy-Efficient Sensor Interface Circuits

Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd