scholarly journals Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 443
Author(s):  
Hongbo Liang ◽  
Shota Maedono ◽  
Yingxin Yu ◽  
Chang Liu ◽  
Naoya Ueda ◽  
...  
Keyword(s):  
Training Session ◽  
Training System ◽  
Eeg Signals ◽  
Shoulder Joints ◽  
Power Spectral ◽  
Training Stage ◽  
Beta Power ◽  
Eeg Changes ◽  
Machine Interface ◽  
Flexion And Extension

Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

scholarly journals Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 81 ◽  
Author(s):  
Maria Rubega ◽  
Fabio Scarpa ◽  
Debora Teodori ◽  
Anne-Sophie Sejling ◽  
Christian S. Frandsen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Single Channel ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Power Spectral ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
Clamp Study

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

2021 ◽  
Vol 11 (3) ◽  
pp. 955-963
Author(s):  
Lixue Yuan ◽  
Yinyan Fan ◽  
Quanxi Gan ◽  
Huibin Feng
Keyword(s):  
Environmental Control ◽  
Low Cost ◽  
Training System ◽  
Brain Machine Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Feedback Information ◽  
Machine Interface ◽  
Neural Feedback

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

scholarly journals A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 119 ◽  
Author(s):  
Konstantinos Tsiakas ◽  
Maria Kyrarini ◽  
Vangelis Karkaletsis ◽  
Fillia Makedon ◽  
Oliver Korn
Keyword(s):  
Training Session ◽  
Training System ◽  
Human Robot Interaction ◽  
Design Development ◽  
Robot Assisted ◽  
Robot Interaction ◽  
Training Systems ◽  
Robot Perception ◽  
Educational Environments ◽  
And Behavior

In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.

scholarly journals Methodology Proposal of ADHD Classification of Children Based on Cross Recurrence Plots

2021 ◽  
Author(s):  
Marco Aceves-Fernandez
Keyword(s):  
Machine Learning ◽  
Spectral Distribution ◽  
High Dimensional ◽  
Control Group ◽  
Machine Learning Method ◽  
Learning Method ◽  
Recurrence Plots ◽  
Eeg Signals ◽  
Power Spectral

Abstract Dealing with electroencephalogram signals (EEG) are often not easy. The lack of predicability and complexity of such non-stationary, noisy and high dimensional signals is challenging. Cross Recurrence Plots (CRP) have been used extensively to deal with detecting subtle changes in signals even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent the proposed methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. Lastly, the results were compared with a baseline machine learning method to prove experimentally that this methodology is consistent and the results repeatable.

scholarly journals Olfactory Stimulation Effect of Aldehydes, Nonanal, and Decanal on the Human Electroencephalographic Activity, According to Nostril Variation

Biomedicines ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 57 ◽  
Author(s):  
Minju Kim ◽  
Kandhasamy Sowndhararajan ◽  
Hae Jin Choi ◽  
Se Jin Park ◽  
Songmun Kim
Keyword(s):  
Olfactory System ◽  
Power Spectra ◽  
Brain Regions ◽  
Eeg Power Spectra ◽  
Beta Power ◽  
Eeg Changes ◽  
Electroencephalographic Activity ◽  
Healthy Participants ◽  
Human Nose ◽  
Better Than

Fragrances play a pivotal role in humans’ psychological and physiological functions through the olfactory system. Aldehydes are important organic compounds with a variety of fragrance notes. Particularly, nonanal (C9) and decanal (C10) aldehydes are important natural fragrant components used to enhance floral, as well as citrus notes in perfumery products. In general, each nostril of the human nose is tuned to smell certain odor molecules better than others due to slight turbinate swelling between the nostrils. Hence, the objective of the present investigation was aimed to evaluate the influence of binasal and uninasal inhalations of C9 and C10 aldehydes on human electroencephalographic (EEG) activity. Twenty healthy participants (10 males and 10 females) participated in this study. The EEG readings were recorded from 8 electrodes (QEEG-8 system) according to the International 10-20 System. The results revealed that C10 exposure exhibited significantly different EEG changes, during binasal and uninasal inhalations. In different brain regions, C10 odor markedly decreased the absolute alpha and absolute beta power spectra. In regards to C9 odor, significant changes of EEG power spectra were noticed only during binasal inhalation. In addition, C10 mainly produced changes at the left parietal site (P3) than other brain sites. In conclusion, the variations in EEG activities of C9 and C10 aldehydes might be owing to their characteristic fragrance quality, as well as the influence of nostril differences.

scholarly journals Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle

2020 ◽  
Vol 10 (10) ◽  
pp. 3532
Author(s):  
Jesús Jaime Moreno Escobar ◽  
Oswaldo Morales Matamoros ◽  
Ricardo Tejeida Padilla ◽  
Ixchel Lina Reyes ◽  
Liliana Chanona Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Doctors ◽  
Eeg Signals ◽  
Smart System ◽  
Brain Signals ◽  
Efficiency Assessment ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Perception Action

This work presents the HSS-Cognitive project, which is a Healthcare Smart System that can be applied in measuring the efficiency of any therapy where neuronal interaction gives a trace whether the therapy is efficient or not, using mathematical tools. The artificial intelligence of the project underlies in the understanding of brain signals or Electroencephalogram (EEG) by means of the determination of the Power Spectral Density (PSD) over all the EEG bands in order to estimate how efficient was a therapy. Our project HSS-Cognitive was applied, recording the EEG signals from two patients treated for 8 min in a dolphin tank, measuring their activity in five experiments and for 6 min measuring their activity in a pool without dolphin in four experiments. After applying our TEA (Therapeutic Efficiency Assessment) metric for patient 1, we found that this patient had gone from having relaxation states regardless of the dolphin to attention states when the dolphin was presented. For patient 2, we found that he had maintained attention states regardless of the dolphin, that is, the DAT (Dolphin Assisted Therapy) did not have a significant effect in this patient, perhaps because he had a surgery last year in order to remove a tumor, having impact on the DAT effectiveness. However, patient 2 presented the best efficiency when doing physical therapy led by a therapist in a pool without dolphins around him. According to our findings, we concluded that our Brain-Inspired Healthcare Smart System can be considered a reliable tool for measuring the efficiency of a dolphin-assisted therapy and not only for therapist or medical doctors but also for researchers in neurosciences.

A portable real-time EEG signal acquisition and tele-medicine system

2020 ◽  
pp. 1-11
Author(s):  
Qiang Zhang ◽  
Peng Wang ◽  
Shanshan Li ◽  
Yonghao Jing
Keyword(s):  
Real Time ◽  
Sql Server ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eeg Data ◽  
Sql Server Database ◽  
High Power Consumption ◽  
Machine Interface ◽  
Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

scholarly journals A study on wavelet analysis of SSVEP Signals

2018 ◽  
Vol 7 (2.25) ◽  
pp. 10
Author(s):  
Bincy Babu ◽  
R Chandrasekaran ◽  
Josline Elsa Joseph ◽  
Thella Shalem Rahul ◽  
T R Thamizhvani ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Spectral Estimation ◽  
Power Spectral Analysis ◽  
Feature Analysis ◽  
Eeg Signals ◽  
Power Spectral ◽  
Frequency Components ◽  
Bci System

Almost every Brain Control Interfcae (BCI) system is framed based on Steady State Visual Evoked Potential (SSVEP) which is predicted through distinguishing overriding frequency components in Electroencephalography (EEG) signals. The proposed system aims in accurate feature extraction of SSVEP signals. Power spectral analysis and wavelet analysis are done for feature analysis. The feature set variation for male and female subjects are obtained. Compared power spectral estimation and wavelet analysis, merits and demerits of each approach can be identified from the outcomes. It offers a theoretical reference of practical choice for BCI application.  

scholarly journals The influence of induction speed on the frontal (processed) EEG

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
D. P. Obert ◽  
P. Sepúlveda ◽  
S. Kratzer ◽  
G. Schneider ◽  
M. Kreuzer
Keyword(s):  
Injection Rate ◽  
Alpha Power ◽  
Verbal Command ◽  
Spectral Edge Frequency ◽  
Induction Group ◽  
Power Spectral ◽  
Beta Power ◽  
Processed Eeg ◽  
Electroencephalogram Eeg ◽  
Slow Injection

Abstract The intravenous injection of the anaesthetic propofol is clinical routine to induce loss of responsiveness (LOR). However, there are only a few studies investigating the influence of the injection rate on the frontal electroencephalogram (EEG) during LOR. Therefore, we focused on changes of the frontal EEG especially during this period. We included 18 patients which were randomly assigned to a slow or fast induction group and recorded the frontal EEG. Based on this data, we calculated the power spectral density, the band powers and band ratios. To analyse the behaviour of processed EEG parameters we calculated the beta ratio, the spectral entropy, and the spectral edge frequency. Due to the prolonged induction period in the slow injection group we were able to distinguish loss of responsiveness to verbal command (LOvR) from loss of responsiveness to painful stimulus (LOpR) whereas in the fast induction group we could not. At LOpR, we observed a higher relative alpha and beta power in the slow induction group while the relative power in the delta range was lower than in the fast induction group. When concentrating on the slow induction group the increase in relative alpha power pre-LOpR and even before LOvR indicated that frontal EEG patterns, which have been suggested as an indicator of unconsciousness, can develop before LOR. Further, LOvR was best reflected by an increase of the alpha to delta ratio, and LOpR was indicated by a decrease of the beta to alpha ratio. These findings highlight the different spectral properties of the EEG at various levels of responsiveness and underline the influence of the propofol injection rate on the frontal EEG during induction of general anesthesia.

Topographic Electroencephalographic Study with Power Ratio Index Mapping in Patients with Malignant Brain Tumors

Neurosurgery ◽  
1985 ◽  
Vol 17 (4) ◽  
pp. 613-619 ◽  
Author(s):  
Ken Nagata ◽  
Cordell E. Gross ◽  
Glenn W. Kindt ◽  
J. Michael Geier ◽  
Geoffrey R. Adey
Keyword(s):  
Brain Tumors ◽  
High Frequency ◽  
Low Frequency ◽  
Power Ratio ◽  
Malignant Brain Tumors ◽  
Power Spectral ◽  
Beta Power ◽  
Index Mapping ◽  
Low Frequency Power ◽  
Frequency Power

Abstract A variant of electroencephalogram (EEG) power spectral mapping called power ratio index (PRI) mapping was used to monitor 15 patients with malignant brain tumors. This index is generated by dividing the low frequency (delta, theta) power by the high frequency (alpha, beta) power. Because the nonparoxysmal effect of a brain tumor on the EEG is reflected as a relative loss of high frequency power and a gain in low frequency power, utilization of the PRI has the effect of placing the epicenter of the “power dysfunction” coincident with the epicenter of the tumor.

Sign in / Sign up

Export Citation Format

Share Document