scholarly journals Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface

2013 ◽  
Vol 34 (6) ◽  
pp. E3 ◽  
Author(s):  
Sumeet Vadera ◽  
Amar R. Marathe ◽  
Jorge Gonzalez-Martinez ◽  
Dawn M. Taylor
Keyword(s):  
Brain Activity ◽  
Intractable Epilepsy ◽  
Brain Structures ◽  
Brain Machine Interface ◽  
Cursor Control ◽  
Spectral Changes ◽  
Power Spectral ◽  
Machine Interface ◽  
The Brain ◽  
Epilepsy Monitoring

Stereoelectroencephalography (SEEG) is becoming more prevalent as a planning tool for surgical treatment of intractable epilepsy. Stereoelectroencephalography uses long, thin, cylindrical “depth” electrodes containing multiple recording contacts along each electrode's length. Each lead is inserted into the brain percutaneously. The advantage of SEEG is that the electrodes can easily target deeper brain structures that are inaccessible with subdural grid electrodes, and SEEG does not require a craniotomy. Brain-machine interface (BMI) research is also becoming more common in the Epilepsy Monitoring Unit. A brain-machine interface decodes a person's desired movement or action from the recorded brain activity and then uses the decoded brain activity to control an assistive device in real time. Although BMIs are primarily being developed for use by severely paralyzed individuals, epilepsy patients undergoing invasive brain monitoring provide an opportunity to test the effectiveness of different invasive recording electrodes for use in BMI systems. This study investigated the ability to use SEEG electrodes for control of 2D cursor velocity in a BMI. Two patients who were undergoing SEEG for intractable epilepsy participated in this study. Participants were instructed to wiggle or rest the hand contralateral to their SEEG electrodes to control the horizontal velocity of a cursor on a screen. Simultaneously they were instructed to wiggle or rest their feet to control the vertical component of cursor velocity. The BMI system was designed to detect power spectral changes associated with hand and foot activity and translate those spectral changes into horizontal and vertical cursor movements in real time. During testing, participants used their decoded SEEG signals to move the brain-controlled cursor to radial targets that appeared on the screen. Although power spectral information from 28 to 32 electrode contacts were used for cursor control during the experiment, post hoc analysis indicated that better control may have been possible using only a single SEEG depth electrode containing multiple recording contacts in both hand and foot cortical areas. These results suggest that the advantages of using SEEG for epilepsy monitoring may also apply to using SEEG electrodes in BMI systems. Specifically, SEEG electrodes can target deeper brain structures, such as foot motor cortex, and both hand and foot areas can be targeted with a single SEEG electrode implanted percutaneously. Therefore, SEEG electrodes may be an attractive option for simple BMI systems that use power spectral modulation in hand and foot cortex for independent control of 2 degrees of freedom.

scholarly journals Use of Brain-Machine Interface technology in the rehabilitation of patients

2020 ◽  
Vol 9 (11) ◽  
pp. e84691110016
Author(s):  
Bruna Corrêa Nolêto ◽  
Fernanda Rodrigues de Araújo Paiva Campelo ◽  
Karleth Costa Spíndola Rodrigues ◽  
Letice Mendes Ribeiro ◽  
Mauricio Salviano
Keyword(s):  
Brain Activity ◽  
Brain Machine Interface ◽  
Human Beings ◽  
Motor Rehabilitation ◽  
Technological Revolution ◽  
Innovative Technologies ◽  
Motor Disability ◽  
Machine Interface ◽  
The Subject ◽  
The Brain

In the last few decades, there have been advances in the field of innovative technologies used for the rehabilitation of people with a motor disability. A great example is the Brain-Machine Interface (BMI) technologies, which allow the control of machines through the brain activity of individuals and contributes to a reorganization of their motor and sensory systems. Thus, several evidences have suggested the use of technologies in the rehabilitation of these patients. The aim of this study was to perform a literature review on the use of technologies applied to motor rehabilitation. To carry out this study, a search for scientific articles was performed in the Pubmed, Scielo and Lilacs databases, in addition to the dissertations and theses found on the CAPES database. There were a total of 24 references, published between 2002 and 2020. According to the literature studied, there is an increase in resources that use technologies as therapeutic options. Many of the conventional interventions are being replaced or associated with these innovative technologies. With the advent of BMI technology and its use in human beings, a technological revolution can be observed in several biomedical areas, thus allowing a multidisciplinary application in the rehabilitation of motor, sensory or cognitive functions in patients. Despite the advances, this subject still shows controversies and before a broad recommendation, more randomized studies and a greater ethical recommendation on the subject will be needed.

scholarly journals A Comprehensive Review on Brain-Computer Interface Controlled Movements

2019 ◽  
Vol 5 (6) ◽  
pp. 3
Author(s):  
Kulsheet Kaur Virdi ◽  
Satish Pawar
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Neuromuscular Disorders ◽  
Research Work ◽  
Brain Structures ◽  
Computer Interface ◽  
Body Parts ◽  
Machine Interface ◽  
The Brain

A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterize the brain activity, because it can be recorded noninvasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. For these applications there is need of such machine learning application which can be efficiently applied on these EEG signals. The aim of this research is review different research work in the field of brain computer interface related to body parts movements.

scholarly journals Home Automation using Brain Computer Interface for the Physically Challenged

2019 ◽  
Vol 9 (1) ◽  
pp. 5018-5021
Keyword(s):  
Brain Computer Interface ◽  
Brain Structures ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Home Automation ◽  
Eeg Signals ◽  
Machine Interface ◽  
Physically Challenged ◽  
The Brain ◽  
Bci System

A Brain-Computer Interface (BCI)is labeledas Mind-Machine Interface (MMI) or a Brain-Machine Interface (BMI). It affords a non-muscular channel of messagein between the computer and a human brain. Using the enhancements in interface equipment to electronics,and the necessity to helpindividuals suffering from disabilities, a new area in this study has begun by acceptingtasks of brain. The Electro-Encephalogram (EEG) is an electrical activity created by brain structures and verified from the scalp using electrodes. The EEG signal is used in actualspell to accomplishperipheral devices using a broad BCI system. The post-processed output signals are converted to suitable instructions to regulate output devices. The main seek is to aidparalyzed and physically immobilizedpersons to govern the home appliances making use of Electro-Encephalogram (EEG) signals, such that they grow to beautonomous. According to the brain responsiveness the devices can be designated then usingrelays, the switching of the home-basedmachinescan be completedconsequently.

scholarly journals Consciousness, decision making, and volition: freedom beyond chance and necessity

2021 ◽  
Author(s):  
Hans Liljenström
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Brain Activity ◽  
Brain Structures ◽  
Complex Process ◽  
Neural Information ◽  
Neural Information Processing ◽  
Stochastic Population Model ◽  
The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

Adaptation of Rehabilitation System Based on User’s Mental Engagement

2015 ◽  
Author(s):  
Ehsan T. Esfahani ◽  
Shrey Pareek ◽  
Pramod Chembrammel ◽  
Mostafa Ghobadi ◽  
Thenkurussi Kesavadas
Keyword(s):  
Brain Activity ◽  
Difficulty Level ◽  
The Novel ◽  
Theta Band ◽  
Robotic Rehabilitation ◽  
Designed Experiments ◽  
Power Spectral ◽  
Rehabilitation System ◽  
Alpha Beta ◽  
The Brain

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated. Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.

scholarly journals NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording

2018 ◽  
Author(s):  
Marc D. Ferro ◽  
Christopher M. Proctor ◽  
Alexander Gonzalez ◽  
Eric Zhao ◽  
Andrea Slezia ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Signal Amplitude ◽  
Brain Machine Interface ◽  
Behavioral Experiments ◽  
Adult Rats ◽  
Integrative Level ◽  
Machine Interface ◽  
Freely Moving ◽  
The Brain

AbstractMinimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time.In this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10µm wide, 1.5µm thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35µm microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation.This research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific sub-populations of neurons, and to develop therapies for a plethora of neurological diseases.

Sign in / Sign up

Export Citation Format

Share Document