Head-related Impulse Response Cues for Spatial Auditory Brain-computer Interface

This study provides a comprehensive test of a head-related impulse response (HRIR) cues for a spatial auditory brain-computer interface (saBCI) speller paradigm. We present a comparison with the conventional virtual sound headphone-based spatial auditory modality. We propose and optimize the three types of sound spatialization settings using a variable elevation in order to evaluate the HRIR efficacy for the saBCI. Three experienced and seven naive BCI users participated in the three experimental setups based on ten presented Japanese syllables. The obtained EEG auditory evoked potentials (AEP) resulted with encouragingly good and stable P300 responses in online BCI experiments. Our case study indicated that users could perceive elevation in the saBCI experiments generated using the HRIR measured from a general head model. The saBCI accuracy and information transfer rate (ITR) scores have been improved comparing to the classical horizontal plane-based virtual spatial sound reproduction modality, as far as the healthy users in the current pilot study are concerned.Comment: 4 pages, 4 figures, accepted for EMBC 2015, IEEE copyrigh

Defining brain–machine interface applications by matching interface performance with device requirements

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications. © 2007 Elsevier B.V. All rights reserved

A Brain Computer Interface for Interactive and Intelligent Image Search and Retrieval

This research proposes a Brain Computer Interface as an interactive and intelligent Image Search and Retrieval tool that allows users, disabled or otherwise to browse and search for images using brain signals. The proposed BCI system implements decoding the brain state by using a non-invasive electroencephalography (EEG) signals, in combination with machine learning, artificial intelligence and automatic content and similarity analysis of images. The user can spell search queries using a mental typewriter (Hex-O-Speller), and the resulting images from the web search are shown to the user as a Rapid Serial Visual Presentations (RSVP). For each image shown, the EEG response is used by the system to recognize the user\u27s interests and narrow down the search results. In addition, it also adds more descriptive terms to the search query, and retrieves more specific image search results and repeats the process. As a proof of concept, a prototype system was designed and implemented to test the navigation through the interface and the Hex-o-Speller using an event-related potential(ERP) detection and classification system. A comparison of different feature extraction methods and classifiers is done to study the detection of event related potentials on a standard data set. The results and challenges faced were noted and analyzed. It elaborates the implementation of the data collection system for the Brain Computer Interface and discusses the recording of events during the visual stimulus and how they are used for epoching/segmenting the data collected. It also describes how the data is stored during training sessions for the BCI. Description of various visual stimuli used during training is also given. The preliminary results of the real time implementation of the prototype BCI system are measured by the number of times the user/subject was successful in navigating through the interface and spelling the search keyword \u27FOX\u27 using the mental-typewriter Hex-O-Speller. Out of ten tries the user/subject was successful six times

Chapter BCI Integration: Application Interfaces

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Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

Feasibility and optimization of a P300-based brain computer interface in individuals with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis is a neuromuscular disease characterized by progressive weakness resulting in a state of profound disability including the loss of functional speech. The rise of new technologies allows people living with ALS and other individuals with severe motor disabilities to communicate using alternate methods. One alternative communication method is an Electroencephalographic (EEG) based brain-computer interface (BCI), which uses a cap embedded with electrodes to read EEG signals. In particular, the P300, a naïve response to stimuli, is used. Through a P300 Speller paradigm, the EEG-based BCI allows individuals with severe disabilities to communicate using a computer even when conventional devices that require mechanical manipulation have failed.An electrode test system was designed to determine whether the commercial electrode cap was functioning correctly. Direct input from a function generator was provided to 4 electrodes at a time and the resulting signal was measured using a DATAQ acquisition box and signal acquisition software. A generated sine was seen in each electrode with a signal loss of 5-6%. The electrodes were able to adjust to and reflect changes in input amplitude and frequency, demonstrating adequacy in signal acquisition.Four able-bodied and eight individuals with ALS from the Philadelphia community participated in the P300 Speller trials under informed consent to determine the feasibility of using the BCI in an ALS population. The EEG was recorded with 8 electrodes using an electrode cap. All aspects of data collection were controlled by the BCI2000 system. Users were asked to participate in a copy-spelling session in which they attended to a specified target letter appearing in a letter matrix. All controls and 6 out of 8 individuals with ALS were considered to be responders (spelling accuracy over 75%). Spelling ability is not correlated to the ALS Functional Rating Scale (ALSFRS), age, or gender. This indicates that individuals who are extremely disabled are able to accurately use a BCI.There are differences in the P300 signal between healthy controls and individuals with ALS. The latency of the peak amplitude of the P300 signal is significantly (p=0.020) later in healthy controls compared to individuals with ALS. The peak amplitude of the P300 signal is not significantly different in healthy controls compared to individuals with ALS. In 3 out of 4 healthy controls, activity can be visualized across all 8 electrodes in the cap whereas in 7 out of 8 individuals with ALS, activity can be visualized primarily in channels 1-4. Changes in latency and signal movement through the electrodes may indicate differences in the electrical wiring in the brain. However, these changes do not affect the ability of an individual to use the BCI and do not influence the amplitude of the signal.The ground and reference electrode locations were changed to determine the flexibility of the BCI and to optimize electrode placement. Examined healthy controls and individuals with ALS were considered responders at each electrode location. The ideal location and number of flashing sequences varies between individuals, however, the ability to move the electrodes without detriment demonstrates that the system can be manipulated to improve comfort and overall satisfaction with the BCI.BCIs can be used by individuals with a debilitating disease such as ALS to communicate with the external world and control their environment. The BCI system and the P300 Speller paradigm are dynamic, flexible, and can be made to work for the majority of individuals with both comfort and ease.M.S., Biomedical Engineering -- Drexel University, 200

BCI Integration: Application Interfaces

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The Effects of Working Memory on Brain-Computer Interface Performance

Amyotrophic lateral sclerosis and other neurodegenerative disorders can cause individuals to lose control of their muscles until they are unable to move or communicate. The development of brain-computer interface (BCI) technology has provided these individuals with an alternative method of communication that does not require muscle movement. Recent research has shown the impact psychological factors have on BCI performance and has highlighted the need for further research. Working memory is one psychological factor that could influence BCI performance. The purpose of the present study is to evaluate the relationship between working memory and brain-computer interface performance. The results indicate that both working memory and general intelligence are significant predictors of BCI performance. This suggests that working memory training could be used to improve performance on a BCI task