1,720,961 research outputs found
A Biofeedback System to Compose Your Own Music While Dancing
No full textBrain Computer Interfaces can enable engaging interactions between different art forms such as music, dance, painting. Building on this, we present a demo of a biofeedback system: a dancer wearing a NeuroSky headset adapts her performance according to the music she listens to. The same music has been generated by a music-composition software depending on her own real-time mental status represented by different fluctuations of some EEG parameters
Brain Computer Interface, Visual Tracker and Artificial Intelligence for a Music Polyphony Generation System
No full textIn the Brain Computer Interface domain, studies on EEG represent a huge field of interest. Interactive systems that exploit low cost electroencephalographs to control machines are gaining momentum. Such technologies can be useful in the field of music and assisted composition. In this paper, a system that aims to generate four-part polyphonies is proposed. An artificial intelligence algorithm permits to generate polyphonies based on the N. Slonimsky’s theory by elaborating data coming from a Leap Motion device, to detect user’s hand movement, and a five-channel EEG signal detection device
EmoSynth Real Time Emotion-Driven Sound Texture Synthesis via Brain-Computer Interface
No full textIn electroacoustic music composition, particularly in sound synthesis techniques, Deep Learning (DL) provides very effective solutions. However, these architectures generally have a high level of automation and use textual language for human interaction. To improve the relationship between composers and artificial intelligence systems, brain-computer interfaces (BCIs) are an effective and direct systems, which have led to considerable improvements in this area. The proposed system employs emotion recognition through electroencephalogram (EEG) signals to control four Variational Autoencoders (VAE) that generate new sound textures. A dataset was acquired using the MUSE2 headset to train four Machine Learning (ML) models capable of classifying human emotions based on Russell's circumplex model. VAEs were trained to produce different sound variations from an audio dataset that allows composers to integrate their sounds. In addition, a graphical user interface (GUI) was developed to facilitate the real-time generation of sound textures, with the support of an external MIDI controller. This GUI continuously provides visual information about the detected emotions and the activity of the left and right brain hemispheres
ARIEL: Brain-Computer Interfaces meet Large Language Models for Emotional Support Conversation
Full text linkIn an era characterized by unprecedented virtual connectivity, paradoxically, individuals often find themselves disconnected from genuine human interactions. The advent of remote working arrangements, compounded by the influence of digital communication platforms, has fostered a sense of isolation among people. Consequently, the prevailing socio-technological landscape has underscored the critical need for innovative solutions to address the emotional void. Conversational systems help people improve their everyday tasks with informative dialogues, and recent applications employ them to target emotional support conversation tasks. Nevertheless, their understanding of human feelings is limited, as they depend solely on information discernible from the text or the users' emotional declarations. Recently, Brain-Computer Interfaces (BCIs), devices that analyze electroencephalographic (EEG) signals, have increasingly become popular given their minimally invasive nature and low cost, besides enabling the detection of users' emotional states reliably. Hence, we propose ARIEL, an emotionAl suppoRt bcI dEvices and Llm-based conversational agent that aims at supporting users' emotional states through conversations and monitoring them via BCI. In this way, it is possible to comprehend the users' feelings reliably, thus making the conversational agent aware of users' emotional evolution during conversations. Our framework makes the LlaMA 2 chat model communicate with an emotion recognition BCI-based system to achieve the emotional support conversation goal. Also, we present a controlled running example that shows the potential of our model and its effective functioning, made possible by a wisely designed hard-prompt strategy. In the future, we will conduct an in-vivo experiment to evaluate the system and its components
Exploring the Mental State Intersection by Brain-Computer Interfaces, Cellular Automata and Biofeedback
No full textBrain-Computer Interfaces (BCI) allows systems to be controlled by signals derived from Electroencephalogram (EEG) analysis. Several low-cost electroencephalographs are available on the market that provides high-quality EEG signals. A very interesting approach in this domain is to represent a user's mental state by using an EEG signal. In this paper, we propose a method to represent and describe the user's mental state by exploiting Cellular Automata (CA). Using BCI, CA, and Autoencoder Neural Network we provide a graphical description and audible representation of the current mental state of the user wearing the device. We demonstrated experimentally the capability of generating a representation of the mental states, both CA and sound texture, while at the same time can be exploited as a biofeedback approach
Predicting Human Emotions using EEG-based Brain computer Interface and Interpretable Machine Learning
Full text linkEEG-based brain-computer interface (BCI) devices have proved to be powerful tools for predicting human emotions. Although Deep learning (DL) techniques have been extensively used to build emotion recognition architectures using EEG-based BCI, they lack interpretability. We propose a prototype of an EEG-based emotion recognition system that can detect the user's emotional state using a deep learning model embedded into an interpretable framework to analyze the decisions of the model and the contributions of the features. The proposed model achieves high performance while showing relevant information on the impact of frequency and spatial features used to predict the emotional states
Predicting Human Emotions using EEG-based Brain computer Interface and Interpretable Machine Learning
No full textEEG-based brain-computer interface (BCI) devices have proved to be powerful tools for predicting human emotions. Although Deep learning (DL) techniques have been extensively used to build emotion recognition architectures using EEG-based BCI, they lack interpretability. We propose a prototype of an EEG-based emotion recognition system that can detect the user's emotional state using a deep learning model embedded into an interpretable framework to analyze the decisions of the model and the contributions of the features. The proposed model achieves high performance while showing relevant information on the impact of frequency and spatial features used to predict the emotional states
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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