Usability and performance measure of a consumer-grade brain computer interface system for environmental control by neurological patients

With the increasing incidence and prevalence of chronic brain injury patients and the current financial constraints in healthcare budgets, there is a need for a more intelligent way to realise the current practice of neuro-rehabilitation service provision. Brain-computer Interface (BCI) systems have the potential to address this issue to a certain extent only if carefully designed research can demonstrate that these systems are accurate, safe, cost-effective, are able to increase patient/carer satisfaction and enhance their quality of life. Therefore, one of the objectives of the proposed study was to examine whether participants (patients with brain injury and a sample of reference population) were able to use a low cost BCI system (Emotiv EPOC) to interact with a computer and to communicate via spelling words. Patients participated in the study did not have prior experience in using BCI headsets so as to measure the user experience in the first-exposure to BCI training. To measure emotional arousal of participants we used an ElectroDermal Activity Sensor (Qsensor by Affectiva). For the signal processing and feature extraction of imagery controls the Cognitive Suite of Emotiv's Control Panel was used. Our study reports the key findings based on data obtained from a group of patients and a sample reference population and presents the implications for the design and development of a BCI system for communication and control. The study also evaluates the performance of the system when used practically in context of an acute clinical environment

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Evaluation of the OpenBCI Neural Interface for Controlling a Quadrotor Simulation

This thesis presents an initial analysis on the use of electroencephalography and electromyography to control the thrust settings of a quadrotor. The OpenBCI neural interface is used to sample muscle activity on a subject's face. Signal processing and event detection algorithms are implemented to identify eyewinks, and these wink events modify the thrust commands in a high fidelity, nonlinear quadrotor simulation. Currently only right and left wink events are detected; these can be mapped to two quadrotor commands such as fly up and down, roll right and left, pitch up and down, or yaw right and left. The ultimate goal of this project is to create a low-cost brain-machine interface system to fully control a real quadrotor using only bioelectrical signals such as electroencephalography and electromyography. A successful demonstration of the OpenBCI system may result in brain-machine interfaces that can be used in the development of low-cost prosthetic arms and legs

Investigación del funcionamiento de electrodos secos y gorro de diseño propio contra gorro comercial con electrodos húmedos aplicando filtros CSP a tareas de movimiento

BCI (Brain Computer Interface) es una tecnología relativamente nueva que se basa en el registro y análisis de las señales eléctricas que se producen en el cerebro (electroencefalograma, EEG). Una vez adquiridas y analizadas se pueden utilizar para diferentes propósitos y utilidades. La adquisición de estas señales se realiza usualmente a través de electrodos que requieren de una preparación previa con la aplicación de geles, molesta en tiempo, y por tener que poner productos en el cabello. Hay otro tipo de electrodos, estos secos y que no requieren preparación previa, pero que tienen un coste muy alto comercialmente. Esto convierte BCI en una tecnología muy costosa como para poder realizar aplicaciones a un precio de mercado asequible. En esta tesis fin de máster se presenta la construcción de un gorro de electrodos secos de bajo coste. Además se realiza una evaluación posterior en la que se comparan los resultados obtenidos por éste ejecutando un protocolo de experimentación basado en efectos motores, con los obtenidos con un gorro comercial de electrodos húmedos. Para este tipo de experimentación se ha implementado un algoritmo CSP (Common Spatial Patterns) que mejora la separabilidad de clases, maximizando/minimizando varianza. Para la evaluación se aplica el coeficiente de determinación R2 con el objetivo de conocer la diferenciabilidad entre las señales grabadas en los experimentos. Además se realiza una clasificación mediante el algoritmo LDA que nos muestra el nivel de discriminación entre esas señales

Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

Micromachined three-dimensional electrode arrays for in-vitro and in-vivo electrogenic cellular networks

This dissertation presents an investigation of micromachined three-dimensional microelectrode arrays (3-D MEAs) targeted toward in-vitro and in-vivo biomedical applications. Current 3-D MEAs are predominantly silicon-based, fabricated in a planar fashion, and are assembled to achieve a true 3-D form: a technique that cannot be extended to micro-manufacturing. The integrated 3-D MEAs developed in this work are polymer-based and thus offer potential for large-scale, high volume manufacturing. Two different techniques are developed for microfabrication of these MEAs - laser micromachining of a conformally deposited polymer on a non-planar surface to create 3-D molds for metal electrodeposition; and metal transfer micromolding, where functional metal layers are transferred from one polymer to another during the process of micromolding thus eliminating the need for complex and non-repeatable 3-D lithography processes. In-vitro and in-vivo 3-D MEAs are microfabricated using these techniques and are packaged utilizing Printed Circuit Boards (PCB) or other low-cost manufacturing techniques. To demonstrate in-vitro applications, growth of 3-D co-cultures of neurons/astrocytes and tissue-slice electrophysiology with brain tissue of rat pups were implemented. To demonstrate in-vivo application, measurements of nerve conduction were implemented. Microelectrode impedance models, noise models and various process models were evaluated. The results confirmed biocompatibility of the polymers involved, acceptable impedance range and noise of the microelectrodes, and potential to improve upon an archaic clinical diagnostic application utilizing these 3-D MEAs.Ph.D.Committee Chair: Mark G. Allen; Committee Member: Elliot L. Chaikof; Committee Member: Ionnis (John) Papapolymerou; Committee Member: Maysam Ghovanloo; Committee Member: Oliver Bran

Low power low noise analog front-end IC design for biomedical sensor interface

Ph.DDOCTOR OF PHILOSOPH