scholarly journals "Thinking out loud": an open-access EEG-based BCI dataset for inner speech recognition

2021 ◽  
Author(s):  
Nicolas Nieto ◽  
Victoria Peterson ◽  
Hugo Leonardo Rufiner ◽  
Juan Kamienkowski ◽  
Ruben Spies
Keyword(s):  
Speech Recognition ◽  
Open Access ◽  
Brain Activity ◽  
Inner Speech ◽  
Human Machine Interaction ◽  
New Techniques ◽  
Inner Voice ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Machine Interaction

Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtained with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Telugu Speech Recognition on LSF and DNN Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 7160-7162
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Deep Level ◽  
Human Interaction ◽  
Major Area ◽  
Feature Classification ◽  
Human Machine Interaction ◽  
Easy Task ◽  
Recognition Systems ◽  
Machine Interaction

This fast world is running with machine and human interaction. This kind of interaction is not an easy task. For proper interaction between human and machine speech recognition is major area where the machine should understand the speech properly to perform the tasks. So ASR have been developed which improvised the HMIS (“Human Machine Interaction systems”) technology in to the deep level. This research focuses on speech recognition over “Telugu language”, which is used in Telugu HMI systems. This paper uses LSF (linear spectral frequencies) technique for feature extraction and DNN for feature classification which finally produced the effective results. Many other recognition systems also used these techniques but for Telugu language this are the most suitable techniques.

scholarly journals Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020 ◽  
Vol 7 ◽  
Author(s):  
Matteo Spezialetti ◽  
Giuseppe Placidi ◽  
Silvia Rossi
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Brain Activity ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Robot Interaction ◽  
Human Machine Interaction ◽  
Recent Advances ◽  
Critical Issues ◽  
Machine Interaction

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

Emulating Subjective Criteria in Corpus Validation

2011 ◽  
pp. 541-546
Author(s):  
Ignasi Iriondo ◽  
Santiago Planet ◽  
Francesc Alías ◽  
Joan-Claudi Socoró ◽  
Elisa Martínez
Keyword(s):  
Speech Synthesis ◽  
Emotional States ◽  
Emotional Speech ◽  
Human Machine Interaction ◽  
Computer Interfaces ◽  
Multimodal Systems ◽  
Specific Emotion ◽  
The One ◽  
Machine Communication ◽  
Machine Interaction

The use of speech in human-machine interaction is increasing as the computer interfaces are becoming more complex but also more useable. These interfaces make use of the information obtained from the user through the analysis of different modalities and show a specific answer by means of different media. The origin of the multimodal systems can be found in its precursor, the “Put-That-There” system (Bolt, 1980), an application operated by speech and gesture recognition. The use of speech as one of these modalities to get orders from users and to provide some oral information makes the human-machine communication more natural. There is a growing number of applications that use speech-to-text conversion and animated characters with speech synthesis. One way to improve the naturalness of these interfaces is the incorporation of the recognition of user’s emotional states (Campbell, 2000). This point generally requires the creation of speech databases showing authentic emotional content allowing robust analysis. Cowie, Douglas-Cowie & Cox (2005) present some databases showing an increase in multimodal databases, and Ververidis & Kotropoulos (2006) describe 64 databases and their application. When creating this kind of databases the main arising problem is the naturalness of the locutions, which directly depends on the method used in the recordings, assuming that they must be controlled without interfering the authenticity of the locutions. Campbell (2000) and Schröder (2004) propose four different sources for obtaining emotional speech, ordered from less control but more authenticity to more control but less authenticity: i) natural occurrences, ii) provocation of authentic emotions in laboratory conditions, iii) stimulated emotions by means of prepared texts, and iv) acted speech reading the same texts with different emotional states, usually performed by actors. On the one hand, corpora designed to synthesize emotional speech are based on studies centred on the listener, following the distinction made by Schröder (2004), because they model the speech parameters in order to transmit a specific emotion. On the other hand, emotion recognition implies studies centred on the speaker, because they are related to the speaker emotional state and the parameters of the speech. The validation of a corpus used for synthesis involves both kinds of studies: the former since it will be used for synthesis and the latter since recognition is needed to evaluate its content. The best validation system is the selection of the valid utterances1 of the corpus by human listeners. However, the big size of a corpus makes this process unaffordable.

Speech-Controlled Human Machine Interaction (Sprachgesteuerte Mensch-Maschine-Interaktion)

2004 ◽  
Vol 46 (6) ◽  
Author(s):  
Jörg Helbig ◽  
Bernd Schindler
Keyword(s):  
Speech Recognition ◽  
Control Systems ◽  
Data Transmission ◽  
Audio Signals ◽  
Human Machine Interaction ◽  
Industrial Environment ◽  
Input And Output ◽  
Speech Control ◽  
Machine Interaction

SummaryThis paper deals with speech controlled applications in an industrial environment. Starting from the application areas the requirements resulting from the technical specialities of this field are described. On the basis of example applications and experiences in the practical use, conclusions for the technological realization of speech control systems are derived. The focus is given to the input and output of the audio signals, the data transmission to the speech recognition computer and to the design of dialogues and vocabularies.

The evolution of passive brain–computer interfaces: enhancing the human–machine interaction

2020 ◽  
pp. 155-179
Author(s):  
Nicolina Sciaraffa ◽  
Pietro Arico ◽  
Gianluca Borghini ◽  
Gianluca Di Flumeri ◽  
Antonio Di Florio ◽  
...  
Keyword(s):  
Brain Computer Interfaces ◽  
Human Machine Interaction ◽  
Computer Interfaces ◽  
Machine Interaction

scholarly journals Westdrive X LoopAR: An Open-Access Virtual Reality Project in Unity for Evaluating User Interaction Methods during Takeover Requests

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1879 ◽  
Author(s):  
Farbod N. Nezami ◽  
Maximilian A. Wächter ◽  
Nora Maleki ◽  
Philipp Spaniol ◽  
Lea M. Kühne ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Open Access ◽  
Force Feedback ◽  
User Interaction ◽  
Human Machine Interaction ◽  
Access Cost ◽  
Cost Efficient ◽  
Further Development ◽  
Machine Interaction ◽  
Traffic Behavior

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human–machine interactions, a mobile, versatile, and cost-efficient setup is needed. Here, we describe a virtual reality toolkit for the Unity 3D game engine containing all the necessary code and assets to enable fast adaptations to various human–machine interaction experiments, including closely monitoring the subject. The presented project contains all the needed functionalities for realistic traffic behavior, cars, pedestrians, and a large, open-source, scriptable, and modular VR environment. It covers roughly 25 km2, a package of 125 animated pedestrians, and numerous vehicles, including motorbikes, trucks, and cars. It also contains all the needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes all necessary scripts for eye-tracking in the used devices. All the main functions are integrated into the graphical user interface of the Unity® editor or are available as prefab variants to ease the use of the embedded functionalities. This project’s primary purpose is to serve as an open-access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators. To ensure the accessibility and usability of the mentioned toolkit, we performed a user experience report, also included in this paper.

Sub-micron pupillometry for optical EEG measurements

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Niels-Ole Rohweder ◽  
Jan Gertheiss ◽  
Christian Rembe
Keyword(s):  
Noise Source ◽  
Pupil Diameter ◽  
Brain Activity ◽  
Frequency Range ◽  
Noise Sources ◽  
Human Machine Interaction ◽  
Pixel Resolution ◽  
Small Oscillations ◽  
Frequency Space ◽  
Machine Interaction

Abstract Recent research indicates that a direct correlation exists between brain activity and oscillations of the pupil. A publication by Park and Whang shows measurements of excitations in the frequency range below 1 Hz. A similar correlation for frequencies between 1 Hz and 40 Hz has not yet been clarified. In order to evaluate small oscillations, a pupillometer with a spatial resolution of 1 µm is required, exceeding the specifications of existing systems. In this paper, we present a setup able to measure with such a resolution. We consider noise sources, and identify the quantisation noise due to finite pixel sizes as the fundamental noise source. We present a model to describe the quantisation noise, and show that our algorithm to measure the pupil diameter achieves a sub-pixel resolution of about half a pixel of the image or 12 µm. We further consider the processing gains from transforming the diameter time series into frequency space, and subsequently show that we can achieve a sub-micron resolution when measuring pupil oscillations, surpassing established pupillometry systems. This setup could allow for the development of a functional optical, fully-remote electroencephalograph (EEG). Such a device could be a valuable sensor in many areas of AI-based human-machine-interaction.

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