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Identifying Hand-based Input Preference Based on Wearable EEGKaining Zhang∗ · 2024Understanding user input preference can improve the user experi- ence, however automatically determining preference can be diffi- cult. In this paper, we designed an EEG-based method for directly evaluating hand-based input preference for touch and mid-air ges- tures on a smartwatch. We conducted a two-phase experiment, recording EEG data from 18 participants as they performed ges- tures and captured their ratings (Phase 1) and preference choices (Phase 2) for each gesture. Our analysis uncovered distinct EEG patterns between preferred and non-preferred gestures, including significant differences in Power Spectral Density (PSD), Coherence (Coh), and Sample Entropy (SE) features. When participants en- gaged with their preferred input gestures, we identified decreased brain activity (PSD) in the central and occipital regions, reduced brain connectivity (Coh) in the delta and alpha bands, and increased brain complexity (SE) in multiple sizes. These insights offer the potential to develop rapid detection of user intent for interactive computing devices by analysing brain signals. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. AHs 2024, April 04–06, 2024, Melbourne, VIC, Australia © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0980-7/24/04 https://doi.org/10.1145/3652920.3653028 CCS CONCEPTS • Human-centered computing →User studies; Gestural input; Laboratory experiments.

UX of AI & PersonalizationResearch Papers

Optimizing Emotion Classification from EEG Signals: A Comparative Analysis of Optimization Techniques on the DEAP DatasetJanki Broker · 2024Electroencephalogram (EEG) signals for emotion classification have gained significant attention in recent times due to its implicit applications in varied fields, including human- computer interaction, affective computing, and substantiated counseling sessions. This study set out to explore the effectiveness of convolutional neural networks (CNNs) in classifying emotions using the DEAP dataset, a publicly available repository of EEG recordings and corresponding emotional annotations by experimenting with different optimizers and loss functions, like "Adamax" and "binary_crossentropy,", etc. By carefully manipulating these optimizers, the study aims to examine and determine the optimal combination with the highest classification accuracy. The findings contribute valuable insights into fine- tuning CNN models for emotion classification based on hyperparameters, paving the way for more robust and effective applications such as personalized diagnosis for counseling, stress- busting techniques and so on. The promising result of this research shows that the “Adamax” optimizer outperforms other optimizers by 82.98 % of the accuracy of classification.

UX of AI & PersonalizationResearch Papers

Affective modelling of users in HCI using EEGJyotish Kumara, Jyoti kumar · 2016Emotions have potential to play a role in HCI which is primarily dominated by cognitive measures. Human physiological communication channels are dominated by emotions. Emotion affects several human activities like communication, learning, decision making, cognition, perception etc. Further, as emotions are difficult to interpret and hard to measure, technologists and designers have been struggling to incorporate them in design and technology. On the other hand, advancement of technology has both necessitated and enabled us to understand emotions and put them to use in contexts like human computer interaction. This study reports an attempt to model emotions by means of electroencephalography (EEG). Video stimuli of four representative basic emotions based on Navarasa theory of Ancient Indian treatise called Natya Shastra were shown to participants and EEG data was collected. Power spectrum analysis of EEG signals associated with emotions was done. Further, the EEG analysis findings were compared with the subject’s self-reports about their emotional states during the experiment. EEG results have shown significantly consistent frequency patterns across the brain lobes for a given emotion. This study suggests that human emotions can be modeled for use in HCI either as an affect assessment tool or for affect based intelligent interactions. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of IHCI 2015.

UX of AI & PersonalizationResearch Papers

Classifying Emotional States through EEG-Derived Spectrograms2024Identifying emotional state of a person plays an important role in a multitude of applications such as affective computing, human-computer interaction and most importantly healthcare. Understanding and correctly identifying human emotions can improve mental health assessments, making it possible to obtain personalized treatment plans and increase the user experience across a variety of digital applications. Our work explores the feasibility of emotion classification using EEG signals recorded from multiple users while experiencing various emotions. Working with time series data of the brain activity in various participants, who are experiencing particular emotions over an interval of 15 seconds. To train and evaluate the classification model several kinds of machine learning and deep learning models such as CNN, RNN and LSTM, are employed. We compare general sequential models with image processing models in the task of classifying signal data. Observing the model’s ability to generalize across the population.

Emotional DesignResearch Papers

Review The Cognitive-Emotional Design and Study of Architectural Space: A Scoping Review of Neuroarchitecture and Its Precursor ApproachesJuan Luis Higuera-Trujillo · 2021Humans respond cognitively and emotionally to the built environment. The modern possibility of recording the neural activity of subjects during exposure to environmental situations, using neuroscientific techniques and virtual reality, provides a promising framework for future design and studies of the built environment. The discipline derived is termed “neuroarchitecture”. Given neuroarchitecture’s transdisciplinary nature, it progresses needs to be reviewed in a contextualised way, together with its precursor approaches. The present article presents a scoping review, which maps out the broad areas on which the new discipline is based. The limitations, controversies, benefits, impact on the professional sectors involved, and potential of neuroarchitecture and its precursors’ approaches are critically addressed.

Emotional DesignResearch Papers