Interfacing the Brain Directly with Musical Systems: On Developing Systems for Making Music with Brain Signals

Leonardo ◽  
2005 ◽  
Vol 38 (4) ◽  
pp. 331-336 ◽  
Author(s):  
Eduardo Reck Miranda ◽  
Andrew Brouse
Keyword(s):  
Computer Music ◽  
Brain Signals ◽  
Musical Systems ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Computer Interfacing

The authors discuss their work on developing technology to interface the brain directly with music systems, a field of research generally known as Brain-Computer Interfacing (BCI). The paper gives a brief background of BCI in general and surveys various attempts at musical BCI, or Brain-Computer Music Interface (BCMI) — systems designed to make music from brain signals, or brainwaves. The authors present a technical introduction to the electroencephalogram (EEG), which measures brainwaves detected by electrodes placed directly on the scalp. They introduce approaches to the design of BCI and BCMI systems and present two case study systems of their own design: the BCMI-Piano and the Inter-Harmonium.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

How reading in single- and multiple-column types influence our cognitive load: an EEG study

2019 ◽  
Vol 37 (4) ◽  
pp. 593-606 ◽  
Author(s):  
Hosam Al-Samarraie ◽  
Atef Eldenfria ◽  
Fahed Zaqout ◽  
Melissa Lee Price
Keyword(s):  
Cognitive Load ◽  
Cognitive Processing ◽  
Content Type ◽  
Brain Signals ◽  
Significant Difference ◽  
Reading Text ◽  
The Impact ◽  
Electroencephalogram Eeg ◽  
Instructional Design Practices ◽  
The Brain

Purpose The impact of different screen-based typography styles on individuals’ cognitive processing of information has not been given much consideration in the literature, though such differences would imply different learning outcomes. This study aims to enrich the current understanding of the impact of reading in single- and multiple-column types on students’ cognitive processing. Design/methodology/approach An electroencephalogram (EEG) was used to record and analyze the brain signals of 27 students while reading from single- and multiple- column layouts. Findings The results showed a significant difference in students’ cognitive load when reading text from different types of columns. All students exerted less processing efforts when text was presented in two-column format, thus experiencing less cognitive load. Originality/value Using EEG, this study examined the neural consequences of reading in single- and multiple-column types on cognitive load during reading. The findings can be used to enrich the current instructional design practices on how different typographical formats facilitate learners’ cognitive performance.

scholarly journals Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Narusci S. Bastos ◽  
Bianca P. Marques ◽  
Diana F. Adamatti ◽  
Cleo Z. Billa
Keyword(s):  
Decision Trees ◽  
Visually Impaired ◽  
Eeg Signals ◽  
Beta Band ◽  
Brain Signals ◽  
Visually Impaired People ◽  
Machine Interface ◽  
Sense Of Touch ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

Brain-Computer Interfacing: A method to detect pain signals in the brain of a patient under operation

2021 ◽  
Author(s):  
Rabbi Sudheer Zacharias ◽  
Anitha S Prasad ◽  
DS Sai Rohith ◽  
Gaurav Simha N ◽  
M.N. Jayaram
Keyword(s):  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Brain–Computer Interfacing with Interactive Systems—Case Study 2

2021 ◽  
pp. 237-272
Author(s):  
A. Vourvopoulos ◽  
E. Niforatos ◽  
S. Bermudez i Badia ◽  
F. Liarokapis
Keyword(s):  
Interactive Systems ◽  
Brain Computer Interfacing ◽  
Computer Interfacing

scholarly journals Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel

2021 ◽  
Vol 14 (01) ◽  
pp. 519-524
Author(s):  
Mohd. Maroof Siddiqui ◽  
Ruchin Jain
Keyword(s):  
Sleep Disorder ◽  
Normal Subjects ◽  
Time Frequency ◽  
Brain Signals ◽  
Eeg Data ◽  
Spectrum Density ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

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