scholarly journals Discovering Patterns in Brain Signals Using Decision Trees

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Narusci S. Bastos ◽  
Diana F. Adamatti ◽  
Cleo Z. Billa
Keyword(s):  
Decision Trees ◽  
Blind People ◽  
Spatial Problem ◽  
Common Belief ◽  
Data Mining Technique ◽  
Computer Interfaces ◽  
Brain Signals ◽  
The Difference ◽  
Difficult Challenge ◽  
The Brain

Even with emerging technologies, such as Brain-Computer Interfaces (BCI) systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain’s behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT) to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain’s behaviour.

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

scholarly journals Brain-Computer Interfaces and Creative Expression: Interface Considerations for Rehabilitative and Therapeutic Interactions

2021 ◽  
Vol 3 ◽  
Author(s):  
Stephanie M. Scott ◽  
Chris Raftery
Keyword(s):  
Personal Experience ◽  
Meaning Making ◽  
Digital Health ◽  
Computer Interface ◽  
Creative Arts ◽  
Embodied Interaction ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Digital Environments ◽  
The Brain

By translating brain signals into new kinds of outputs, Brain-Computer Interface (BCI) systems hold tremendous potential as both transformative rehabilitation and communication tools. BCIs can be considered a unique technology, in that they are able to provide a direct link between the brain and the external environment. By affording users with opportunities for communication and self-expression, BCI systems serve as a bridge between abled-bodied and disabled users, in turn reducing existing barriers between these groups. This perspective piece explores the complex shifting relationship between neuroadaptive systems and humans by foregrounding personal experience and embodied interaction as concepts through which to evaluate digital environments cultivated through the design of BCI interfaces. To underscore the importance of fostering human-centered experiences through technologically mediated interactions, this work offers a conceptual framework through which the rehabilitative and therapeutic possibilities of BCI user-system engagement could be furthered. By inviting somatic analysis towards the design of BCI interfaces and incorporating tenets of creative arts therapies practices into hybrid navigation paradigms for self-expressive applications, this work highlights the need for examining individual technological interactions as sites with meaning-making potentiality, as well as those conceived through unique exchanges based on user-specific needs for communication. Designing BCI interfaces in ways that afford users with increased options for navigation, as well as with the ability to share subjective and collective experiences, helps to redefine existing boundaries of digital and physical user-system interactions and encourages the reimagining of these systems as novel digital health tools for recovery.

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.

Brain-Computer Interfaces and Visual Activity

2013 ◽  
pp. 1549-1570
Author(s):  
Carmen Vidaurre ◽  
Andrea Kübler ◽  
Michael Tangermann ◽  
Klaus-Robert Müller ◽  
José del R. Millán
Keyword(s):  
Video Game ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Robot Arm ◽  
Virtual Keyboard ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Brain States ◽  
Physically Disabled People ◽  
The Brain

There is growing interest in the use of brain signals for communication and operation of devices, in particular, for physically disabled people. Brain states can be detected and translated into actions such as selecting a letter from a virtual keyboard, playing a video game, or moving a robot arm. This chapter presents what is known about the effects of visual stimuli on brain activity and introduces means of monitoring brain activity. Possibilities of brain-controlled interfaces, either with the brain signals as the sole input or in combination with the measured point of gaze, are discussed.

scholarly journals Investigation of Electrical Interference towards Phosphene-Based Walking Support System

2021 ◽  
Vol 12 (3) ◽  
pp. 2178-2183
Author(s):  
Manami, K Et.al
Keyword(s):  
Support System ◽  
Short Distance ◽  
Visual Pathway ◽  
Electrical Stimulus ◽  
Single Pair ◽  
Blind People ◽  
The Difference ◽  
The Brain ◽  
Electrical Interference

A walking support system with phosphenes for blind people has been investigated. Phosphene is a phenomenon where a flash of light is recognized in the brain by giving an electrical stimulus to human’s visual pathway. Phosphenes can be perceived even if their eyes are closed or they are blind. It has been clarified that phosphenes can be induced to several directions if electrodes placements are precisely selected. When phosphenes are presented to two directions for recognizing two obstacles, two pairs of electrodes must be applied. In such a case, however, the electrical interference occurs due to the short distance between electrodes. In the practical use of the phosphene-based walking support system, the avoidance of electrical interference is significant in order to present the phosphenes precisely. Therefore, in this paper, we first practically investigate the electrical interference by considering the difference in phosphene induction generated by a single pair of electrodes and by two pairs of electrodes. Then, the solutions to avoid the electrical interference are discussed.

scholarly journals Brain Computing Interface using Deep Learning for Blind People

2019 ◽  
Vol 8 (4) ◽  
pp. 8227-8230
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Research Work ◽  
Physical World ◽  
Computer Applications ◽  
Blind People ◽  
Data Mining Technique ◽  
Developing Area ◽  
Difficult Challenge

The developing area of research in Brain Computer Interface (BCI) is used to enhance the quality of human computer applications. It can be decoding individuals by the computer device signals converted into commands between human’s neural world and outer physical world. The brain use bodies under some circumstances to interact with the external world and also brain can be depressed of their sensing abilities namely blindness or deafness. In this study, analyze of brain’s behavior using BCI for blind people in spatial activity. The common beliefs in blind people using other senses by compensate their lack of vision. In case of BCI system can able to understand the brain’s activity even in very difficult challenge. Therefore we propose the data mining technique. In this research work, deep learning approach based on the framework of Convolution Neural Networks (CNN) with Long Short-Term Memory (LSTM) can help us to discover their brain’s activity for blind people.

scholarly journals Progress in Brain Computer Interface: Challenges and Opportunities

2021 ◽  
Vol 15 ◽  
Author(s):  
Simanto Saha ◽  
Khondaker A. Mamun ◽  
Khawza Ahmed ◽  
Raqibul Mostafa ◽  
Ganesh R. Naik ◽  
...  
Keyword(s):  
Affective Computing ◽  
Global Scale ◽  
Communication Link ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Challenges And Opportunities ◽  
Significant Research ◽  
Potential Applications ◽  
Related Feature ◽  
The Brain

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Brain-Computer Interfaces and Visual Activity

2012 ◽  
pp. 153-174
Author(s):  
Carmen Vidaurre ◽  
Andrea Kübler ◽  
Michael Tangermann ◽  
Klaus-Robert Müller ◽  
José del R. Millán
Keyword(s):  
Video Game ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Robot Arm ◽  
Virtual Keyboard ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Brain States ◽  
Physically Disabled People ◽  
The Brain

There is growing interest in the use of brain signals for communication and operation of devices – in particular, for physically disabled people. Brain states can be detected and translated into actions such as selecting a letter from a virtual keyboard, playing a video game, or moving a robot arm. This chapter presents what is known about the effects of visual stimuli on brain activity and introduces means of monitoring brain activity. Possibilities of brain-controlled interfaces, either with the brain signals as the sole input or in combination with the measured point of gaze, are discussed.

scholarly journals Are Brain–Computer Interfaces Feasible With Integrated Photonic Chips?

2022 ◽  
Vol 15 ◽  
Author(s):  
Vahid Salari ◽  
Serafim Rodrigues ◽  
Erhan Saglamyurek ◽  
Christoph Simon ◽  
Daniel Oblak
Keyword(s):  
High Speed ◽  
Near Infrared ◽  
Light Emission ◽  
Photon Emission ◽  
High Yield ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Computer Based ◽  
Neuronal Functions ◽  
The Brain

The present paper examines the viability of a radically novel idea for brain–computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.

scholarly journals The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

2021 ◽  
Author(s):  
Zachary Freudenburg ◽  
Khaterah Kohneshin ◽  
Erik Aarnoutse ◽  
Mariska Vansteensel ◽  
Mariana Branco ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Brain Activity ◽  
Daily Basis ◽  
Error Awareness ◽  
Cursor Control ◽  
Computer Interfaces ◽  
Brain Signals ◽  
User Intentions ◽  
Related Potentials ◽  
The Brain

AbstractWhile brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.

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