Central Auditory Processing Disorder: Towards a Therapeutic EEG Neurofeedback Brain Computer Interface

Central auditory processing (CAP) refers to the process of integrating and processing auditory signals in the central auditory nervous system. Problems with CAP are thought to underlie central auditory processing disorder (CAPD) which is associated with specific populations of adults and children who demonstrate poor performance on tasks. CAPD is typically diagnosed in individuals with poor auditory perception who also show no physical problems with their inner ear, outer ear, or cochlea (Keilman et al., 2013; Koravand et al 2013). CAPD is characterized by an impaired ability to filter out background noise and distinguish between different auditory stimuli, and is often comorbid with other neurological disorders (Kim & Chung, 2013; Strauss et al., 2008). Exciting new research has shown improvements those identified with CAPD-like disorders can improve speech comprehension, harmonic recognition, and sound localization, just by engaging in behaviors which are associated with CAP (Alain et al., 2014; Anderson et al., 2013). The overarching aim of this research was to create a online electroencephalography (EEG) brain computer interface (BCI) that could be used by anyone, not just those who show CAPD symptomatology, to gain increased performance on central auditory processing tasks

Development of Music-Imagery Brain-Computer Interface (BCI): Decoding Pitch Imagery from Electroencephalogram and Closed-loop BCI Training

Department of Biomedical Engineering (Human Factors Engineering)Music is one of the most instinctual functions in human beings throughout the ancient and contemporary eras, and musical ability is correlated with other comprehensive cognitive abilities, such as language, visuospatial cognition, memory, etc. Indeed, the loss of musical ability, provoked by brain damage such as stroke, shows the overall decline of other cognitive functions. Thus, the obligation to restore the lost musical ability has been arising. Thanks to the active brain-computer interfaces (BCIs), restoring impaired sensorimotor and cognitive functions is viable, posing the potent recovery of musical ability from post-stroke cognitive impairments (PSCIs). However, it is the pitch that is related to comprehensive musical abilities, and pitch sensation training induces recovery in musical ability also in behavioral studies. Thus, it can be postulated that the development of the music-imagery BCI (MI-BCI), especially using the pitch as the main framework, would empower the musical ability improvement and restoration for the people who lost or has lack musical ability, the amusics. However, it remains largely unknown whether it is feasible to decode imagined musical information directly from neural activity. Therefore, Study1 in this dissertation aimed to decode pitch information directly from scalp electroencephalography (EEG). Twenty healthy participants performed a task to imagine one of the seven musical pitches (C4 ??? B4) randomly. To find EEG features for pitch imagination, we took two approaches: exploring multi-band spectral power at individual channels (IC) and exploring power differences between bilaterally symmetric channels (DC). We classified these features into the seven-pitch classes using various types of classifiers. The selected spectral power features revealed marked contrasts between left and right hemispheres, between low-, ( 12 Hz) bands, and between frontal and parietal areas. The best classification performance for seven pitches was obtained using the IC feature and SVM with an average accuracy of 35.68??7.47% (max. 50%) and the average information transfer rate (ITR) of 0.37??0.22 bits/sec. Yet, when we decoded a different number of classes (K = 2 ~ 6) by grouping adjacent pitches, ITR was similar across K as well as between IC and DC features, suggesting the efficiency of DC features. This study would be the first to demonstrate the feasibility of decoding imagined musical pitches directly from human EEG. Closed-loop training system via BCI is growing the interest in the field associated with BCI usages, such as science, engineering, and medical fields. Especially, the neurofeedback strategy has enabled the BCI system users to attempt actively by self-regulating their brain pattern to a certain imagery task, with the merit of training effect reinforcing the more effective rehabilitation possibilities and the enhancing the BCI performances. With an optimized neurofeedback design according to the specific aim that the experimenter hoping to observe the training effect, the neurofeedback would be effective, and the system would possess the training effect. The purpose of this study is to investigate if the closed-loop neurofeedback system via BCI, named BrainCoder, is able to possess the training effect and if the designed neurofeedback method is appropriate. 5 subjects in total were recruited and conducted 5 times within 10 days. The total experiment was held 25 times, but 1 was excluded due to the device problem. The BrainCoder system had 2 sessions, the open-loop and closed-loop session, and 3 tasks. In the open-loop session, the task same as Study1 was conducted to build the decoding model, and Neurofeedback Task (NFT) was held in the closed-loop session, with the task playing the song by the BrainCoder was preceded and followed. The NFT consisted of runs and trials, and the task was to control the visual cursor and finally locate it to the desired position of a run within 7 trials. 63 runs were held in total. The accuracy of the closed-loop session of BrainCoder was 38.43??1.68%, and the number of trials that succeeded in correcting the location was 3.34??0.02 on average. Moreover, the result of this study verified the validity of the BrainCoder system and neurofeedback effect by comparing the result with a random model generated from the existing data. Also, the difference among the group was tested to figure out if the musical ability was associated with the performance of the system. This study demonstrates the feasibility of the closed-loop neurofeedback training via EEG BCI and is expected to be the fundamental study with the initial development of a closed-loop Music-Imagery-based BCI training system.ope

Mechanisms of neurofeedback: a computation-theoretic approach

Neurofeedback training is a form of brain training in which information about a neural measure is fed back to the trainee who is instructed to increase or decrease the value of that particular measure. This paper focuses on electroencephalography (EEG) neurofeedback in which the neural measures of interest are the brain oscillations. To date, the neural mechanisms that underlie successful neurofeedback training are still unexplained. Such an understanding would benefit researchers, funding agencies, clinicians, regulatory bodies, and insurance firms. Based on recent empirical work, an emerging theory couched firmly within computational neuroscience is proposed that advocates a critical role of the striatum in modulating EEG frequencies. The theory is implemented as a computer simulation of peak alpha upregulation, but in principle any frequency band at one or more electrode sites could be addressed. The simulation successfully learns to increase its peak alpha frequency and demonstrates the influence of threshold setting – the threshold that determines whether positive or negative feedback is provided. Analyses of the model suggest that neurofeedback can be likened to a search process that uses importance sampling to estimate the posterior probability distribution over striatal representational space, with each representation being associated with a distribution of values of the target EEG band. The model provides an important proof of concept to address pertinent methodological questions about how to understand and improve EEG neurofeedback success

Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of Designers

Intuition, metacognition, and subjective uncertainty interact in complex ways to shape the creative design process. Design intuition, a designer's innate ability to generate creative ideas and solutions based on implicit knowledge and experience, is often evaluated and refined through metacognitive monitoring. This self-awareness and management of cognitive processes can be triggered by subjective uncertainty, reflecting the designer's self-assessed confidence in their decisions. Despite their significance, few creativity support tools have targeted the enhancement of these intertwined components using biofeedback, particularly the affect associated with these processes. In this study, we introduce "Multi-Self," a BCI-VR design tool designed to amplify metacognitive monitoring in architectural design. Multi-Self evaluates designers' affect (valence and arousal) to their work, providing real-time, visual biofeedback. A proof-of-concept pilot study with 24 participants assessed its feasibility. While feedback accuracy responses were mixed, most participants found the tool useful, reporting that it sparked metacognitive monitoring, encouraged exploration of the design space, and helped modulate subjective uncertainty

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201

The N400 for Brain Computer Interfacing: complexities and opportunities

The N400 is an Event Related Potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. Given this potential it is notable that the BCI literature exploiting the N400 is limited. We identify three existing application areas: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.Comment: 28 pages, 2 figures, 2 table

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Electrophysiological assessment methodology of sensory processing dysfunction in schizophrenia and dementia of the Alzheimer type

Schizophrenia and Alzheimer’s disease impacts on various sensory processings are extensively reviewed in the present publication. This article describes aspects of a research project whose aim is to delineate the neurobiology that may underlie Social Withdrawal in Alzheimer’s disease, Schizophrenia and Major Depression. This is a European-funded IMI 2 project, identified as PRISM (Psychiatric Ratings using Intermediate Stratified Markers). This paper focuses specifically on the selected electrophysiological paradigms chosen based on a comprehensive review of all relevant literature and practical constraints. The choice of the electrophysiological biomarkers were fundamentality based their metrics and capacity to discriminate between populations. The selected electrophysiological paradigms are resting state EEG, auditory mismatch negativity, auditory and visual based oddball paradigms, facial emotion processing ERP’s and auditory steady-state response. The primary objective is to study the effect of social withdrawal on various biomarkers and endophenotypes found altered in the target populations. This has never been studied in relationship to social withdrawal, an important component of CNS diseases