Low noise multichannel amplifier for portable EEG biomedical applications

Badillo, Leonardo; Ponomaryov, Volodymyr; Ramos, E.; Igartua, Luis

doi:10.1109/iembs.2003.1280852

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Newer systems can use dry electrodes or saline-based electrodes. EEG amplifiers can have varying parameters like noise performance, power consumption, signal bandwidth, and cost (Badillo et al, 2003; Harrison and Charles, 2003; Lin et al, 2006; Hairston et al, 2015). More recent developments include wireless data transition directly from a cap (De Vos et al, 2014), ultra-low power digitization, and a minimal use of cables (Warchall et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Melnik

Legkov

Izdebski

et al. 2017

Front. Hum. Neurosci.

101

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Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.

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Section: Introductionmentioning

confidence: 99%

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Melnik

Legkov

Izdebski

et al. 2017

Front. Hum. Neurosci.

101

View full text Add to dashboard Cite

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“…The active low pass and high pass filter are having four resistors and two capacitors. 31,32 A non-inverting gain amplifier is used where the frequency fc is determined by using the formula given in equation ( 3) where R and C are the Resistor and capacitor for the filter. The derivation of low pass and high pass filter is given in equations ( 4) and (5).…”

Section: Block Diagram Of the Proposed Systemmentioning

confidence: 99%

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Archana

Rajalakshmi

2022

Proc Inst Mech Eng H

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Single-channel Electrooculogram (EOG) is proposed for detecting diabetic retinopathy. The Corneal-retinal potential of the eyes plays a vital role in the acquisition of Electrooculography. Diabetes is the most prevalent disease and for one out of three people with diabetes above 40 years, diabetic retinopathy occurs. It is necessary for the early detection of diabetic retinopathy as it is one of the primary reasons for blindness in the population. The potential difference between cornea and retina leads to the acquisition of EOG signal. The proposed study aims to design a low-cost miniaturized hardware circuit to obtain EOG signal using second order filters without compromising in accuracy of the outcome signal and to classify the signal into normal and diabetic retinopathy subjects by extracting the statistical features like kurtosis, mean, median absolute deviation, standard deviation, and range from software filtered EOG signal. Among the classifiers used, Support vector machine (SVM) shows a higher accuracy of 93.33%. The sensitivity, specificity and Area Under Curve (AUC) values of SVM are 96.43%, 90.625%, 0.93% is considered as more favorable outcome for the proposed method and it supports the developed prototype and processing methodology. The novelty of the research is based on proposing and exploring a non-invasive methodology for Diabetic retinopathy diagnosis based on EOG signal. Thus, the designed hardware is simple in operation and cost effective, provides an affordable and non-invasive diagnostic tool for diabetic retinopathy patients

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