scholarly journals Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand

2021 ◽  
Vol 15 ◽  
Author(s):  
Chad Bouton ◽  
Nikunj Bhagat ◽  
Santosh Chandrasekaran ◽  
Jose Herrero ◽  
Noah Markowitz ◽  
...  
Keyword(s):  
Feature Selection ◽  
White Matter ◽  
Minimally Invasive ◽  
Brain Computer Interface ◽  
Temporal Correlation ◽  
Computer Interface ◽  
Human Hand ◽  
Individual Finger ◽  
Tactile Stimuli ◽  
The Brain

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.

scholarly journals Identifying and Decoding Temporally Correlated Neural Patterns in the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand Using Stereoelectroencephalography

2021 ◽  
Author(s):  
Chad Bouton ◽  
Nikunj Bhagat ◽  
Santosh Chandrasekaran ◽  
Jose Herrero ◽  
Noah Markowitz ◽  
...  
Keyword(s):  
Feature Selection ◽  
White Matter ◽  
Minimally Invasive ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Finger Movements ◽  
Temporal Autocorrelation ◽  
Individual Finger ◽  
Tactile Stimuli ◽  
The Brain

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore function in patients living with debilitating conditions. One of the challenges currently facing BCI technology, however, is how to minimize surgical risk. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications since they can lead to fewer complications. SEEG electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. We therefore investigated the viability of using SEEG electrodes in a BCI for recording and decoding neural signals related to movement and the sense of touch and compared its performance to electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns were variable trial-to-trial and transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on temporal autocorrelation, a repeatability metric. An algorithm based on temporal autocorrelation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling both transient and sustained input features. Combining temporal autocorrelation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 +/- 1.51% for hand movements, up to 91.69 +/- 0.49% for individual finger movements, and up to 80.64 +/- 1.64% for focal tactile stimuli to the finger pads and palm while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wider variety of conditions.

scholarly journals Introduction of brain computer interface to neurologists

2021 ◽  
Vol 23 (2) ◽  
pp. 92-98
Author(s):  
Do-Hyung Kim ◽  
Hong Gi Yeom ◽  
Minjung Kim ◽  
Seung Hwan Kim ◽  
Tae-Won Yang ◽  
...  
Keyword(s):  
Motor Control ◽  
Minimally Invasive ◽  
Word Processing ◽  
Brain Computer Interface ◽  
Arm Movements ◽  
Computer Interface ◽  
Neurological Rehabilitation ◽  
Electrical Signals ◽  
Brain Waves ◽  
The Brain

A brain-computer interface (BCI) is a technology that acquires and analyzes electrical signals from the brain to control external devices. BCI technologies can generally be used to control a computer cursor, limb orthosis, or word processing. This technology can also be used as a neurological rehabilitation tool for people with poor motor control. We reviewed historical attempts and methods toward predicting arm movements using brain waves. In addition, representative studies of minimally invasive and noninvasive BCI were summarized.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

Cerebral Pulse Controlled Robotic Hand for Effective Paralytic Movements

2021 ◽  
Vol 9 (8) ◽  
pp. 2201-2204
Author(s):  
Abhay Patil
Keyword(s):  
Basic Assumption ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Handicapped People ◽  
Alpha Beta

Abstract: There are roughly 21 million handicapped people in India, which is comparable to 2.2% of the complete populace. These people are affected by various neuromuscular problems. To empower them to articulate their thoughts, one can supply them with elective and augmentative correspondence. For this, a Brain-Computer Interface framework (BCI) has been assembled to manage this specific need. The basic assumption of the venture reports the plan, working just as a testing impersonation of a man's arm which is intended to be powerfully just as kinematically exact. The conveyed gadget attempts to take after the movement of the human hand by investigating the signs delivered by cerebrum waves. The cerebrum waves are really detected by sensors in the Neurosky headset and produce alpha, beta, and gamma signals. Then, at that point, this sign is examined by the microcontroller and is then acquired onto the engineered hand by means of servo engines. A patient that experiences an amputee underneath the elbow can acquire from this specific biomechanical arm. Keywords: Brainwaves, Brain Computer Interface, Arduino, EEG sensor, Neurosky Mindwave Headset, Robotic arm

scholarly journals A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers

2015 ◽  
Vol 87 (4) ◽  
pp. 1929-1937 ◽  
Author(s):  
Regina O. Heidrich ◽  
Emely Jensen ◽  
Francisco Rebelo ◽  
Tiago Oliveira
Keyword(s):  
Cerebral Palsy ◽  
Comparative Study ◽  
Case Studies ◽  
Brain Computer Interface ◽  
Product Evaluation ◽  
Computer Interface ◽  
Healthy Controls ◽  
The Brain

ABSTRACT This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

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