EEG-Based Brain-Computer Interfacing via Motor-Imagery: Practical Implementation and Feature Analysis

The human brain is the most intriguing and complex signal processing unit ever known to us. A unique characteristic of our brain is its plasticity property, i.e., the ability of neurons to modify their behavior (structure and functionality) in response to environmental diversity. The plasticity property of brain has motivated design of brain-computer interfaces (BCI) to develop an alternative form of communication channel between brain signals and the external world. The BCI systems have several therapeutic applications of significant importance including but not limited to rehabilitation/ assistive systems, rehabilitation robotics, and neuro-prosthesis control. Despite recent advancements in BCIs, such systems are still far from being reliably incorporated within humanmachine inference networks. In this regard, the thesis focuses on Motor Imagery (MI)-based BCI systems with the objective of tackling some key challenges observed in existing solutions. The MI is defined as a cognitive process in which a person imagines performing a movement without peripheral (muscle) activation. At one hand, the thesis focuses on feature extraction, which is one of the most crucial steps for the development of an effective BCI system. In this regard, the thesis proposes a subject-specific filtering framework, referred to as the regularized double-band Bayesian (R-B2B) spectral filtering. The proposed R-B2B framework couples three main feature extraction categories, namely filter-bank solutions, regularized techniques, and optimized Bayesian mechanisms to enhance the overall classification accuracy of the BCI. To further evaluate the effects of deploying optimized subject-specific spectra-spatial filters, it is vital to examine and investigate different aspects of data collection and in particular, effects of the stimuli provided to subjects to trigger MI tasks. The second main initiative of the thesis is to propose an element of experimental design dealing with MI-based BCI systems. In this regard, we have implemented an EEG-based BCI system and constructed a benchmark dataset associated with 10 healthy subjects performing actual movement and MI tasks. To investigate effects of stimulus on the overall achievable performance, four different protocols are designed and implemented via introduction of visual and voice stimuli. Finally, the work investigates effects of adaptive trimming of EEG epochs resulting in an adaptive and subject-specific solution

Design, implementation, evaluation and application of a 32-channel radio frequency signal generator for thermal magnetic resonance based anti-cancer treatment

Thermal Magnetic Resonance (ThermalMR) leverages radio frequency (RF)-induced heating to examine the role of temperature in biological systems and disease. To advance RF heating with multi-channel RF antenna arrays and overcome the shortcomings of current RF signal sources, this work reports on a 32-channel modular signal generator (SG(PLL)). The SG(PLL) was designed around phase-locked loop (PLL) chips and a field-programmable gate array chip. To examine the system properties, switching/settling times, accuracy of RF power level and phase shifting were characterized. Electric field manipulation was successfully demonstrated in deionized water. RF heating was conducted in a phantom setup using self-grounded bow-tie RF antennae driven by the SG(PLL). Commercial signal generators limited to a lower number of RF channels were used for comparison. RF heating was evaluated with numerical temperature simulations and experimentally validated with MR thermometry. Numerical temperature simulations and heating experiments controlled by the SG(PLL) revealed the same RF interference patterns. Upon RF heating similar temperature changes across the phantom were observed for the SG(PLL) and for the commercial devices. To conclude, this work presents the first 32-channel modular signal source for RF heating. The large number of coherent RF channels, wide frequency range and accurate phase shift provided by the SG(PLL) form a technological basis for ThermalMR controlled hyperthermia anti-cancer treatment

A TECHNOLOGY ASSESSMENT OF BRAIN-COMPUTER INTERFACES. BRIDGING PRESENT AND FUTURE WITH A HUMAN-CENTERED PERSPECTIVE

Technology assessment is a systematic approach used to scientifically investigate the conditions and consequences of technology and technicization while determining its social evaluation. This research focuses on the evaluation of an emerging technology, Brain-Computer Interface (BCI), which enables direct communication between the brain and an external device. As an emerging technology, BCI is in its early stages of research, facing numerous challenges. To address the assessment of BCIs, a method-ology combining Constructive Technology Assessment (CTA) and Foresight within the umbrella con-cept of Future-oriented Technology Analysis (FTA), has been developed and applied. This thesis con-ducts a literature review and applies both structured, open-ended interviews and a survey seeking an-swers to these issues. It explores various social, ethical, legal, and philosophical issues to be addressed in the field of BCIs, both in the present as well as in the future. Understanding the key challenges, de-velopments, and potential future trajectories of this technology is essential to grasp how its applications can offer both opportunities and threats to society at large. The research addresses the concerns of both the Technology Assessment and Brain-Computer Interface communities, offering a comprehensive un-derstanding of how these social, ethical, legal, and philosophical issues may evolve over time. Perspec-tives from various key stakeholders in the BCI field, as well as neurotechnologies in the context of as-sistive technologies, are examined, providing valuable insights for further research in this area.A avaliação de tecnologia é uma abordagem sistemática usada para investigar cientificamente as condições e consequências da tecnologia e da tecnicização, ao mesmo tempo que determina sua avaliação social. Esta pesquisa concentra-se na avaliação de uma tecnologia emergente, a Interface Cérebro-Computador (BCI), que possibilita a comunicação direta entre o cérebro e um dispositivo externo. Como tecnologia emergente, a BCI está em seus estágios iniciais de pesquisa, enfrentando inúmeros desafios. Para abordar a avaliação das BCIs, foi desenvolvida e aplicada uma metodologia que combina a Avaliação Construtiva de Tecnologia (CTA) e a Prospectiva, dentro do conceito geral de Análise de Tecnologia Orientada para o Futuro (FTA). Esta tese realiza uma revisão de literatura e aplica tanto entrevistas estruturadas e abertas quanto um questionário na busca por respostas para estas questões. Ela explora várias questões sociais, éticas, legais e filosóficas a serem abordadas no campo das BCIs, tanto no presente como no futuro. Compreender os principais desafios, desenvolvimentos e possíveis trajetórias futuras dessa tecnologia é essencial para compreender como suas aplicações podem oferecer oportunidades e ameaças à sociedade em geral. A pesquisa aborda as preocupações das comunidades de Avaliação de Tecnologia e Interface Cérebro-Computador, oferecendo uma compreensão abrangente de como essas questões sociais, éticas, legais e filosóficas podem evoluir ao longo do tempo. Perspectivas de diversos atores-chave no campo de BCI, bem como neurotecnologias no contexto de tecnologias assistivas, são examinadas, fornecendo informações valiosas para pesquisas futuras nessa área

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

BRAIN COMPUTER INTERFACE (BCI) ON ATTENTION: A SCOPING REVIEW

Technological innovations are now an integral part of healthcare. Brain-computer interface (BCI) is a novel technological intervention system that is useful in restoring function to people disabled by neurological disorders such as attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. This paper surveys the literature concerning the effectiveness of BCI on attention in subjects under various conditions. The findings of this scoping review are that studies have been made on ADHD, ALS, ASD subjects, and subjects recovering from brain and spinal cord injuries. BCI based neurofeedback training is seen to be effective in improving attention in these subjects. Some studies have also been made on healthy subjects.BCI based neurofeedback training promises neurocognitive improvement and EEG changes in the elderly. Different cognitive assessments have been tried on healthy adults.   From this review, it is evident that hardly any research has been done on using BCI for enhancing attention in post-stroke subjects. So there arises the necessity for making a study on the effects of BCI based attention training in post-stroke subjects, as attention is the key for learning motor skills that get impaired following a stroke. Currently, many researches are underway to determine the effects of a BCI based training program for the enhancement of attention in post-stroke subjects

Modeling of bone conduction of sound in the human head using hp-finite elements: Code design and verification

We focus on the development of a reliable numerical model for investigating the bone-conduction of sound in the human head. The main challenge of the problem is the lack of fundamental knowledge regarding the transmission of acoustic energy through non-airborne pathways to the cochlea. A fully coupled model based on the acoustic/elastic interaction problem with a detailed resolution of the cochlea region and its interface with the skull and the air pathways, should provide an insight into this fundamental, long standing research problem. To this aim we have developed a 3D hp-finite element code that supports elements of all shapes (tetrahedra, prisms and pyramids) to better capture the geometrical features of the head. We have tested the code on a multilayered sphere and employed it to solve an idealized model of head. In the future we hope to attack a model with a more realistic geometry

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies