scholarly journals Quality Assessment of Single-Channel EEG for Wearable Devices

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 601 ◽  
Author(s):  
Fanny Grosselin ◽  
Xavier Navarro-Sune ◽  
Alessia Vozzi ◽  
Katerina Pandremmenou ◽  
Fabrizio De Vico Fallani ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Single Channel ◽  
Low Cost ◽  
Wearable Devices ◽  
Brain State ◽  
Eeg Signals ◽  
Many Sources ◽  
The Brain

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

scholarly journals Using time causal quantifiers to characterize sleep stages

2019 ◽  
Author(s):  
Diego M. Mateos ◽  
Jaime Gómez-Ramírez ◽  
Osvaldo A. Rosso
Keyword(s):  
Temporal Dynamics ◽  
Single Channel ◽  
Well Being ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Sleep Recording ◽  
Vector Approach

AbstractSleep plays substantial role in daily cognitive performance, mood and memory. The study of sleep has attracted the interest of neuroscientists, clinicans and the overall population, with increasing number of adults suffering from insufficient amounts of sleep. Sleep is an activity composed of different stages whose temporal dynamics, cycles and inter dependencies are not fully understood. Healthy body function and personal well being, however, depends on proper unfolding and continuance of the sleep cycles. The characterization of the different sleep stages can be undertaken with the development of biomarkers derived from sleep recording. For this purpose, in this work we analyzed single-channel EEG signals from 106 healthy subjects. The signals were quantified using the permutation vector approach using five different information theoretic measures: i) Shannon’s entropy, ii) MPR statistical complexity, iii) Fisher information, iv) Renyí Min-entropy and v) Lempel-Ziv complexity. The results show that all five information theory-based measures make possible to quantify and classify the underlying dynamics of the different sleep stages. In addition to this, we combine these measures to show that planes containing pairs of measures, such as the plane composed of Lempel-Ziv and Shannon, have a better performance for differentiating sleep states than measures used individually for the same purpose.

A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data

2017 ◽  
Vol 29 (4) ◽  
pp. 84-102 ◽  
Author(s):  
Vandana Roy ◽  
Shailja Shukla
Keyword(s):  
Single Channel ◽  
Diagnostic Procedures ◽  
Ensemble Empirical Mode Decomposition ◽  
Motion Artifacts ◽  
Eeg Signals ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Healthcare Model

The Big data as Electroencephalography (EEG) can induce by artifacts during acquisition process which will obstruct the features and quality of interest in the signal. The healthcare diagnostic procedures need strong and viable biomedical signals and elimination of artifacts from EEG is important. In this research paper, an improved ensemble approach is proposed for single channel EEG signal motion artifacts removal. Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis (EEMD-CCA) filter combination are applied to remove artifact effectively and further Stationary Wavelet Transform (SWT) is applied to remove the randomness and unpredictability due to motion artifacts from EEG signals. This new filter combination technique was tested against currently available artifact removal techniques and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use.

Efficient approach for optical and morphologic characterization of hybrid perovskite films based on reflectance and transmittance measurements

2021 ◽  
Author(s):  
Jorge Caram ◽  
Maximiliano Senno ◽  
Luisa Cencha ◽  
Silvia Tinte ◽  
Raul Urteaga ◽  
...  
Keyword(s):  
Low Cost ◽  
Normal Incidence ◽  
Film Deposition ◽  
Morphological Properties ◽  
Efficient Procedure ◽  
Force Microscopy ◽  
Atomic Force ◽  
Hybrid Perovskite

Abstract Organo-inorganic perovskites have been intensively studied due to its potential application in low cost and great efficient energy conversion in solar cells. Despite the great improvement in the quality of organo-inorganic perovskite films, a wide dispersion into the same batch of perovskite-based devices keep being an obstacle to obtaining highly reproducible results. For that reason new and efficient strategies for testing deposition results is imperative for the next step. Here we present a simple and efficient procedure for characterizing the optical and morphological properties based on the simultaneous reflectance and transmittance measurements under normal incidence over a MAPbI3 film. The proposed method provides qualitative and quantitative morphological information associated with the film roughness as well as information about the position of the optical gap and possible contributions to the optical dispersion in the structure that can be used as a simple diagnostic tool to optimize the film deposition. Results are contrasted and validated with electronic and atomic force microscopy, as well as first-principles calculations.

Increasing the value of a biorefinery based on hot-water extraction: Lignin products

TAPPI Journal ◽  
2012 ◽  
Vol 11 (1) ◽  
pp. 19-26 ◽  
Author(s):  
BILJANA M. BUJANOVIC ◽  
MANGESH J. GOUNDALKAR ◽  
THOMAS E. AMIDON
Keyword(s):  
Carbon Fibers ◽  
Aromatic Compounds ◽  
Low Cost ◽  
Hot Water ◽  
Water Extraction ◽  
Hot Water Extraction ◽  
Isolation And Characterization ◽  
Potential Applications

In conventional pulping technologies, lignin is used mainly as a low-cost source of energy. Small quantities of industrially produced lignin are used for the production of chemicals and materials. Biorefinery technologies are emerging that have an ultimate goal of replacing fossil sources for the production of fuels and other products. To achieve this goal effectively, biorefinery technologies must take advantage of lignin as the most abundant natural aromatic polymer and use it to add higher-value products to product portfolios. Lignin has the potential to be used in making a broad range of high-quality products, including carbon fibers, thermoplastics, and oxygenated aromatic compounds. Existing processes focus primarily on the quality of cellulose and result in a severely modified and contaminated lignin of relatively low value. Lignin produced in more flexible biorefinery operations is more uniform and less contaminated than currently available industrial lignins, opening the door for broader applications of lignin and lignin products. The results of isolation and characterization of lignin dissolved during hot-water extraction and some potential applications of this lignin are discussed.

scholarly journals Single-Channel Region-Based Speller for Controlling Home Appliances

2022 ◽  
pp. 541-569
Author(s):  
Praveen Kumar Shukla ◽  
Rahul Kumar Chaurasiya ◽  
Shrish Verma
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
P300 Speller ◽  
Cord Injury ◽  
Control Translation ◽  
Ann Classifier ◽  
The Brain ◽  
Channel Region ◽  
Bci System ◽  
Lateral Sclerosis

The brain-computer interface (BCI) system uses electroencephalography (EEG) signals for correspondence between the human and the outside world. This BCI communication system does not require any muscle action; hence, it can be controlled with the help of brain activities only. Therefore, this kind of system is helpful for patients, who are completely paralyzed or suffering from diseases like ALS (Amyotrophic Lateral Sclerosis), and spinal cord injury, etc., but having a normal functioning brain. A region-based P300 speller system for controlling home electronic appliances is proposed in this article. With the help of the proposed system, users can control and use appliances like an electronic door, fan, light, system, etc., without carrying out any physical movement. The experiments are conducted for five, ten, and fifteen trails for each subject. Among all classifiers, the ANN classifier provides the best off-line experiment accuracy of the order of 80% for fifteen flashes. Moreover, for the control translation, the Arduino module is also designed which is low cost and low power-based and physically controlled a device.

scholarly journals Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes

2020 ◽  
Vol 37 (5) ◽  
pp. 831-837
Author(s):  
Mesut Melek ◽  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Digital Signals ◽  
Eeg Signals ◽  
The Gaze ◽  
Information Transfer Rate ◽  
The Brain ◽  
Bci System

Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

scholarly journals Test-Bench for the Characterization of Flexion Sensors Used in Biomechanics

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2994
Author(s):  
Monica Tiboni ◽  
Azzurra Filippini ◽  
Cinzia Amici ◽  
David Vetturi
Keyword(s):  
Statistical Analysis ◽  
Test Bench ◽  
Low Cost ◽  
Wearable Devices ◽  
Radius Of Curvature ◽  
Structural Components ◽  
Good Repeatability ◽  
Fixed Radius ◽  
Biomedical Field

The design, prototyping and validation of an innovative test bench for the characterization and the hysteresis measurement of flexion sensors are presented in this paper. The device, especially designed to test sensors employed in the biomedical field, can be effectively used to characterize also sensors intended for other applications, such as wearable devices. Flexion sensors are widely adopted in devices for biomedical purposes and in this context are commonly used in two main ways: to measure movements (i) with fixed radius of curvature and (ii) with variable radius of curvature. The test bench has been conceived and designed with reference to both of these needs of use. The technological choices have been oriented towards simplicity of manufacture and assembly, configuration flexibility and low cost of realization. For this purpose, 3D printing technology was chosen for most of the structural components of the device. To verify the test bench performances, a test campaign was carried out on five commercial bending sensors. To characterize each sensor, the acquired measurements were analysed by assessing repeatability and linearity of the sensors and hysteresis of the system sensor/test bench. A statistical analysis was performed to study the positioning repeatability and the hysteresis of the device. The results demonstrate good repeatability and low hysteresis.

scholarly journals Characterization of Biodiesel from Alkaline Refinement of Waste Cooking Oil

2020 ◽  
Vol 10 (1) ◽  
pp. 16-24
Author(s):  
Aliru Olajide Mustapha ◽  
Amina Abiola Adebisi ◽  
Bukola Opeyemi Olanipekun
Keyword(s):  
Fatty Acids ◽  
Low Cost ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Fuel Quality ◽  
Cooking Oil ◽  
Yield And Quality ◽  
American Standard

The waste cooking oil (WCO) is a low cost and prospective feedstock with no competitive food uses for biodiesel production, but the yield and quality have been greatly affected by impurities.  This study examined the chemical and fuel quality of biodiesel of both WCO and alkaline treated WCO.  The transesterification process using the alkaline treated cooking oil (ACO) methanol and sodium hydroxide as catalyst followed the Association of Officials of Analytical Chemists (AOAC) techniques. The pH values between 7.27 and 8.65 were found for alkaline treated cooking methyl ester (ACME), alkaline treated cooking oil (ACO) and WCO. Density of ACME, ACO and WCO varied between 0.89 and 0.93 (g/cm3). The fatty acids found were benzoic acid (3.77%), octanoic acid (8.35%), and palmitic acid (75.02%) – most abundant. Comparison of results with the American Standard for Testing Materials (ASTM) values showed quality enhancements of ACO in physicochemical and fuel properties over WCO. The biodiesels from ACO have enhanced emulsification, fuel and free fatty acids qualities over the WCO, showing the refinement methodology of WCO has overall improvement in the biodiesel purity and quality against the previous conflicting reports.

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