Immersive Composition for Sensory Rehabilitation: 3D Visualisation, Surround Sound, and Synthesised Music to Provoke Catharsis and Healing

There is a wide range of sensory therapies using sound, music and visual stimuli. Some focus on soothing or distracting stimuli such as natural sounds or classical music as analgesic, while other approaches emphasize the active performance of producing music as therapy. This paper proposes an immersive multi-sensory Exposure Therapy for people suffering from anxiety disorders, based on a rich, detailed surround-soundscape. This soundscape is composed to include the users’ own idiosyncratic anxiety triggers as a form of habituation, and to provoke psychological catharsis, as a non-verbal, visceral and enveloping exposure. To accurately pinpoint the most effective sounds and to optimally compose the soundscape we will monitor the participants’ physiological responses such as electroencephalography, respiration, electromyography, and heart rate during exposure. We hypothesize that such physiologically optimized sensory landscapes will aid the development of future immersive therapies for various psychological conditions, Sound is a major trigger of anxiety, and auditory hypersensitivity is an extremely problematic symptom. Exposure to stress-inducing sounds can free anxiety sufferers from entrenched avoidance behaviors, teaching physiological coping strategies and encouraging resolution of the psychological issues agitated by the sound

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed

To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on human-computer interaction (HCI) design and recommender system

Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers

This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field, covering tasks, algorithms, and datasets. By synthesizing recent findings, our aim is to offer a fundamental understanding of BCI research, identifying promising avenues for future investigations.Comment: 14 pages, 1 figure, HCI International 2023 Conferenc

A multiplex connectivity map of valence-arousal emotional model

high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users’ emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants’ ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes

The NeuroDante Project: Neurometric measurements of participant’s reaction to literary auditory stimuli from dante’s “divina commedia”

Neurodante. Progetto di analisi neurometrica di alcuni brani della Commedi

A Systematic Review of The Potential Use of Neurofeedback in Patients with Schizophrenia.

Schizophrenia (SCZ) is a neurodevelopmental disorder characterized by positive symptoms (hallucinations and delusions), negative symptoms (anhedonia, social withdrawal) and marked cognitive deficits (memory, executive function, and attention). Current mainstays of treatment, including medications and psychotherapy, do not adequately address cognitive symptoms, which are essential for everyday functioning. However, recent advances in computational neurobiology have rekindled interest in neurofeedback (NF), a form of self-regulation or neuromodulation, in potentially alleviating cognitive symptoms in patients with SCZ. Therefore, we conducted a systematic review of the literature for NF studies in SCZ to identify lessons learned and to identify steps to move the field forward. Our findings reveal that NF studies to date consist mostly of case studies and small sample, single-group studies. Despite few randomized clinical trials, the results suggest that NF is feasible and that it leads to measurable changes in brain function. These findings indicate early proof-of-concept data that needs to be followed up by larger, randomized clinical trials, testing the efficacy of NF compared to well thought out placebos. We hope that such an undertaking by the field will lead to innovative solutions that address refractory symptoms and improve everyday functioning in patients with SCZ