Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern

Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P (p < 0.05)

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods: We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results: The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion: The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment

Effects of Visual Attention on Tactile P300 BCI.

Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant’s left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p< 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. These findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation

On Riemannian tools for classification improvement in Brain-Computer Interfaces

A Brain Computer Interface (BCI) or Brain Machine Interface (BMI) is a device that allows the exchange of information between the brain of a person and a computer without the need of physical interaction. This technology promises to change the way in which we interact with machines, but it is not yet affordable, robust or quick enough to substitute other classic human to machine interfaces for the general public. This being said, the lack of need of interaction makes them a very promising solution that would provide people with severe motor disabilities with a new way of interacting with their surroundings, improving their quality of life. The most extended method of extracting information about brain activity and the one used for this project is the Electroencefalogram (EEG). This device consists of multiple electrodes mounted on a helmet-like structure that is placed on the user’s scalp. The electrodes detect the sum of action potentials from large populations of neurons on the brain’s cortex. The main advantages of this technique are the relative low cost of the device, portability, and the high temporal resolution and ease of use of a non invasive technique. This is not free of disadvantages, as the method suffers from a low signal to noise ratio, low robustness to interference, low spatial resolution and the effects of inter and intra session drift, that is, the movement of the electrodes during and between sessions produce variations on the acquisition of the signal. There are also multiple paradigms in the field of BCI, each one of them focusing on a different brain signal. This work is centered around the Motor Imagery Brain Computer Interface (MI-BCI), which differs from other BCIs in the fact that it directly decodes the intention of the user without the need of inducing a specific response in the brain by presenting an stimulus. This approach is considered to be more natural and can be more comfortable, but also requires a higher level of mental effort and proficiency from part of the user. The MI-BCI is based on a signal of unknown origin that is produced on the sensorymotor cortex, responsible for voluntary movements and touch among others, the Sensorimotor Rhythms (SMR). This signal is atenuated when the person performs or thinks about performing a movement, which is called an Event Related Desynchronization (ERD) and amplified when going back to the idling state, an Event Related Synchronization (ERS). As the brain is a distributed system, the origin of these events can be estimated and is related to the movement that the person imagined. In an implementation, these movements are limited to a discrete set of posibilities and each one is mapped to a computer instruction, allowing the unidirectional transfer of information between brain and machine. The classical machine learning approach to this problem has been to use very specific signal processing techniques to extract relevant features for this problem that can then be fed to a general classification algorithm. The main tecnique is known as Common Spatial Patterns (CSP) followed by classification with Linear Discriminant Analysis (LDA) or Support Vector Machine (SVM). This has some advantages such as a relative low requirement of training samples, but also lacks the capability of generalisation, and a system fine tuned for one user cannot be used for other users or even for another session from the same user reliably. In this work we study an alternative framework that uses the covariance matrices of the EEG signals as observations and exploits the Riemannian geometry of Symmetric Positive Definite (SPD) matrices to classify them in their natural space. This is not only a more general signal processing approach that has been used in other fields of research, but also opens the possibility of transfering some information between users and sessions, which may result in a more robust system or in a system that requires less data for training. This is crucial for the usability of MI-BCI because recording a training session before each use of the system is mentally exhausting and time consuming.Universidad de Sevilla. Máster Universitario en Ingeniería de Telecomunicació

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Digital Interaction and Machine Intelligence

This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction

Subtle, intimate interfaces for mobile human computer interaction

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 113-122).The mobile phone is always carried with the user and is always active: it is a very personal device. It fosters and satisfies a need to be constantly connected to one's significant other, friends or business partners. At the same time, mobile devices are often used in public, where one is surrounded by others not involved in the interaction. This private interaction in public is often a cause of unnecessary disruption and distraction, both for the bystanders and even for the user. Nevertheless, mobile devices do fulfill an important function, informing of important events and urgent communications, so turning them off is often not practical nor possible. This thesis introduces Intimate Interfaces: discreet interfaces that allow subtle private interaction with mobile devices in order to minimize disruption in public and gain social acceptance. Intimate Interfaces are inconspicuous to those around the users, while still allowing them to communicate. The concept is demonstrated through the design, implementation and evaluation of two novel devices: * Intimate Communication Armband - a wearable device, embedded in an armband, that detects motionless gestures through electromyographic (EMG) sensing for subtle input and provides tactile output;(cont.) * Notifying Glasses - a wearable notification display embedded in eyeglasses; it delivers subtle cues to the peripheral field of view of the wearer, while being invisible to others. The cues can convey a few bits of information and can be designed to meet specific levels of visibility and disruption. Experimental results show that both interfaces can be reliably used for subtle input and output. Therefore, Intimate Interfaces can be profitably used to improve mobile human-computer interaction.by Enrico Costanza.S.M

Two-Dimensional Electronics - Prospects and Challenges

During the past 10 years, two-dimensional materials have found incredible attention in the scientific community. The first two-dimensional material studied in detail was graphene, and many groups explored its potential for electronic applications. Meanwhile, researchers have extended their work to two-dimensional materials beyond graphene. At present, several hundred of these materials are known and part of them is considered to be useful for electronic applications. Rapid progress has been made in research concerning two-dimensional electronics, and a variety of transistors of different two-dimensional materials, including graphene, transition metal dichalcogenides, e.g., MoS2 and WS2, and phosphorene, have been reported. Other areas where two-dimensional materials are considered promising are sensors, transparent electrodes, or displays, to name just a few. This Special Issue of Electronics is devoted to all aspects of two-dimensional materials for electronic applications, including material preparation and analysis, device fabrication and characterization, device physics, modeling and simulation, and circuits. The devices of interest include, but are not limited to transistors (both field-effect transistors and alternative transistor concepts), sensors, optoelectronics devices, MEMS and NEMS, and displays

Optogenetic Interrogation and Manipulation of Vascular Blood Flow in Cortex

Understanding blood flow regulatory mechanisms that correlate the regional blood flow with the level of local neuronal activity in brain is an ongoing research. Discerning different aspects of this coupling is of substantial importance in interpretation of functional imaging results, such as functional magnetic resonance imaging (fMRI), that rely on hemodynamic recordings to detect and image brain neuronal activity. Moreover, this understanding can provide insight into blood flow disorders under different pathophysiological conditions and possible treatments for such disorders. The blood regulatory mechanisms can be studied at two different; however, complementary levels: at the cellular level or at the vascular level. To fully understand the regulatory mechanisms in brain, it is essential to discern details of the coupling mechanism in each level. While, the cellular pathways of the coupling mechanism has been studied extensively in the past few decades, our understanding of the vascular response to brain activity is fairly basic. The main objective of this dissertation is to develop proper methods and instrumentation to interrogate regional cortical vasodynamics in response to local brain stimulation. For this purpose we offer the design of a custom-made OCT scanner and the necessary lens mechanisms to integrate the OCT system, fluorescence imaging, and optogenetic stimulation technologies in a single system. The design uses off-the-shelf components for a cost-effective design. The modular design of the device allows scientists to modify it in accordance with their research needs. With this multi-modal system we are able to monitor blood flow, blood velocity, and lumen diameter of pial vessels, simultaneously. Additionally, the system design provides the possibility of generating arbitrary spatial stimulation light pattern on brain. These abilities enables researchers to capture more diverse datasets and, eventually, obtain a more comprehensive picture of the vasodynamics in the brain. Along with the device we also proposed new biological experiments that are tailored to investigate the spatio-temporal properties of the vascular response to optical neurostimulation of the excitatory neurons. We demonstrate the ability of the proposed methods to investigate the effect of length and amplitude of stimulation on the temporal pattern of response in the blood flow, blood velocity, and diameter of the pial vessels. Moreover, we offer systemic approaches to investigate the spatial characteristics of the response in a vascular network. In these methods we apply arbitrary spatial patterns of optical stimulation to the cortex of transgenic mice and monitor the attributes of surrounding vessels. With this flexibility we were able to image the brain region that is influenced by a pial artery. After characterizing the spatio-temporal properties of the vascular blood flow response to optical neuro-modulation, we demonstrate the design and application of an optogenetic-based closed-loop controller mechanism in the brain. This controller, uses a proportional–integral–derivative (PID) compensator to engineer temporal optogenetic stimulation light pulses and maintain the flow of blood at various user defined levels in a set of selected arteries. Upon tuning the gain values of the PID controller we obtained a near to critically-damped response in the blood flow of selected arterial vessels

Modulation of in Vivo Neural Network Activity with Electrochemically Controlled Delivery of Neuroactive Molecules

Neural interface technologies with implantable microelectrode arrays hold great promise for treating neural injuries or disorders. On neural electrode surfaces, conducting polymers can be electropolymerization with negatively charged molecules incorporated. When the polymer is reduced with negative current, dopant molecules are released from the polymer. This feature can be utilized to deliver neural transmitters and modulators from the electrodes to alter neural network activity. Previously, release of CNQX (6-cyano-7-nitroquinoxaline-2,3-dione), an AMPA (2-amino-3-(5-methyl-3-oxo-1,2- oxazol-4-yl)propanoic acid) receptor antagonist in hippocampal neuron culture effectively suppressed local neural activity in a transient manner. In this study, we further advance this technology by characterizing the drug loading and release capacity from microelectrodes, expanding the range of candidate dopants, and demonstrating in vivo effectiveness in rat somatosensory (S1) barrel cortex. Firstly, to quantify the concentration of released drug, fluorescent model molecule was used and quantitatively assessed in a real time imaging system. Stimulation amplitude was varied to determine the amount of released drug from microelectrodes. Secondly, only negatively charged drugs have been effectively released in the past. In this study, zwitterionic transmitter γ-Aminobutyric acid (GABA) was successfully delivered with the technique, greatly expanding the applicable range for the technique. Finally, we used evoked response from barrel cortex to evaluate the release of DNQX (6,7-dinitroquinoxaline-2,3-dione), an analog of CNQX. The neural activity of barrel cortex reliably represents sensory stimuli from whiskers, hence provides an excellent in vivo network model for evaluating our neurochemical release system. Neural activity from multi-whisker stimulation was immediately and locally suppressed by released DNQX for one to six seconds, demonstrating the high spatial-temporal resolution of the technique. Furthermore, weaker activities were nearly abolished by released DNQX whilst stronger activities were less influenced, because the strong over-saturated neural input can only be partially antagonized. The system demonstrates successful modulation of neural network activity in a highly controllable manner. With the ease of being incorporated in existing neural implants without increasing the volume or complexity, this technology may find use in a wide range of neuroscience studies and potentially therapeutic devices