Decoding of continuous movement attempt in 2-dimensions from non-invasive low frequency brain signals

Abstract Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

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.

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HEART RATE VARIABILITY RELATED TO N95 RESPIRATOR USE IN INTERNS DURING COVID-19 PANDEMIC

10.36106/ijsr/4308359 ◽

2021 ◽

pp. 69-70

Author(s):

Pakanati Sujana ◽

Venkata Mahesh Gandhavalla ◽

K. Prabhakara Rao

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Low Frequency ◽

Protective Equipment ◽

Short Term ◽

Non Invasive ◽

Critical Components ◽

The Individual ◽

N95 Respirators ◽

Respiratory Droplets

Introduction: COVID19 is caused by SARS-CoV-2 which is primarily transmitted through respiratory droplets and contact routes. WHO recommended the use of personal protective equipment (PPE) for prevention and N95 respirators are critical components of PPE. Breathing through N95 respirator will impart stress in the individual and that can be assessed by heart rate variability (HRV). HRV measures the variation in time between each heartbeat controlled by autonomic nervous system (ANS), which is a non invasive reliable index to identify the ANS imbalances. Aims And Objectives: This study is aimed at assessing the HRV of Interns working in COVID19 wards using N95 respirators. Methodology: This study included 100 interns in whom short term HRV was recorded using the standard protocol. Lead II of ECG was recorded using AD instruments (ADI) 8channel polygraph and HRV was analysed using Labchart 8pro software. The recordings were taken before and 1hour after wearing N95 respirator. Results: Overall HRV (SDRR) was found to decrease signicantly after wearing N95 respirator for 1hr (p=0.000). Similarly, indices representing the parasympathetic component ( RMSSD and HF ) were also found to decrease signicantly with the use of N95 respirator. Low frequency (LF) power and LF/HF ratio increased signicantly with N95 respirator use (p=0.000). Conclusion: We conclude that using N95 respirator increased sympathetic activity reecting decreased HRV in our subjects Hence we recommend that it is better to change the duty pattern for interns.

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EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Brain Sciences ◽

10.3390/brainsci8110199 ◽

2018 ◽

Vol 8 (11) ◽

pp. 199 ◽

Cited By ~ 6

Author(s):

Rodrigo Ramele ◽

Ana Villar ◽

Juan Santos

Keyword(s):

Brain Computer Interface ◽

Waveform Analysis ◽

Computer Interface ◽

Clinical Tool ◽

Non Invasive ◽

Computer Interfaces ◽

Brain Signals ◽

Public Dataset ◽

Imaging Sensor ◽

Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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Natural Sensations Evoked in Distal Extremities Using Surface Electrical Stimulation

The Open Biomedical Engineering Journal ◽

10.2174/1874120701812010001 ◽

2018 ◽

Vol 12 (1) ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Julia P. Slopsema ◽

John M. Boss ◽

Lane A. Heyboer ◽

Carson M. Tobias ◽

Brooke P. Draggoo ◽

...

Keyword(s):

Electrical Stimulation ◽

Low Frequency ◽

Pulse Amplitude ◽

Neural Stimulation ◽

Phantom Limb ◽

Neural Function ◽

Medical Treatments ◽

Non Invasive ◽

Knee Strength ◽

Better Than

Background: Electrical stimulation is increasingly relevant in a variety of medical treatments. In this study, surface electrical stimulation was evaluated as a method to non-invasively target a neural function, specifically natural sensation in the distal limbs. Method: Electrodes were placed over the median and ulnar nerves at the elbow and the common peroneal and lateral sural cutaneous nerves at the knee. Strength-duration curves for sensation were compared between nerves. The location, modality, and intensity of each sensation were also analyzed. In an effort to evoke natural sensations, several patterned waveforms were evaluated. Results: Distal sensation was obtained in all but one of the 48 nerves tested in able-bodied subjects and in the two nerves from subjects with an amputation. Increasing the pulse amplitude of the stimulus caused an increase in the area and magnitude of the sensation in a majority of subjects. A low frequency waveform evoked a tapping or tapping-like sensation in 29 out of the 31 able-bodied subjects and a sensation that could be considered natural in two subjects with an amputation. This waveform performed better than other patterned waveforms that had proven effective during implanted extra-neural stimulation. Conclusion: Surface electrical stimulation has the potential to be a powerful, non-invasive tool for activation of the nervous system. These results suggest that a tapping sensation in the distal extremity can be evoked in most able-bodied individuals and that targeting the nerve trunk from the surface is a valid method to evoke sensation in the phantom limb of individuals with an amputation for short term applications.

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Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

10.1101/2021.12.16.472983 ◽

2021 ◽

Author(s):

Joaquin Gonzalez ◽

Diego M. Mateos ◽

Matias Cavelli ◽

Alejandra Mondino ◽

Claudia Pascovich ◽

...

Keyword(s):

Slow Wave Sleep ◽

Low Frequency ◽

Neuronal Population ◽

Complexity Measures ◽

Low Frequencies ◽

New Approach ◽

Frequency Bands ◽

Cortical Oscillations ◽

Brain Signals ◽

Low Frequency Oscillations

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

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Statistical analysis of non-invasive Low Frequency EIT human measurements of euronal activity

IFMBE Proceedings - 13th International Conference on Electrical Bioimpedance and the 8th Conference on Electrical Impedance Tomography ◽

10.1007/978-3-540-73841-1_141 ◽

2007 ◽

pp. 548-551

Author(s):

O. Gilad ◽

L. Horesh ◽

S. Akselrod ◽

David S. Holder

Keyword(s):

Statistical Analysis ◽

Low Frequency ◽

Non Invasive

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Reversal of Renal Insufficiency in an Aging Cat: A 5-year Multi-Crossover Case Study

The Journal of Science and Medicine ◽

10.37714/josam.v2i2.30 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Robert Dennis ◽

John Dennis

Keyword(s):

Palliative Care ◽

Renal Insufficiency ◽

Treatment Options ◽

Low Frequency ◽

Standard Of Care ◽

Omega 3 ◽

Current Standard ◽

Invasive Treatment ◽

Non Invasive ◽

Electro Magnetic

Renal failure is a leading cause of suffering and death in domestic cats, with approximately 1 in 3 cats affected. Current standard-of-care treatment usually involves palliative care, diets restricted in protein and phosphorus, plenty of fluids, and sometimes vitamin D and Omega-3. But even with early detection, which is difficult, treatment options are limited and often are not very effective. Dietary restrictions and palliative care are often the best that can be offered, but the creatinine levels tend to inexorably creep upward toward eventual kidney failure and death. We report the effectiveness of the use of a low-frequency, low-intensity, non-invasive treatment using Pulsed Electro-Magnetic Fields, specifically tuned to inductively generate micro-electric currents in deep tissues (ICES®-PEMF). This report chronicles the return to normal and then reversion to renal insufficiency in a single cat, when ICES®-PEMF was applied, then withheld, then applied again, over three cycles of application and non-application, over a 5-year period. A return to normal creatinine levels, with a subsequent return to renal insufficiency as indicated by loss of control of creatinine, correlated precisely with the application and non-application of ICES®-PEMF. The pattern observed during each cycle was as follows: when applied 2 to 3 times weekly for 20-60 minutes each treatment, creatinine levels declined to normal range within 2-3 months. During periods when treatment was discontinued, creatinine levels began to climb to high levels again. We suggest the further study and potential use of ICES®-PEMF as an effective, inexpensive, safe, non-invasive treatment for feline kidney disease.

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The Use of Portable EEG Devices in Development of Immersive Virtual Reality Environments for Converting Emotional States into Specific Commands

Proceedings ◽

10.3390/proceedings2020054043 ◽

2020 ◽

Vol 54 (1) ◽

pp. 43

Author(s):

Catarina Sá ◽

Paulo Veloso Gomes ◽

António Marques ◽

António Correia

Keyword(s):

Virtual Reality ◽

Portable Devices ◽

Immersive Virtual Reality ◽

Emotional States ◽

Social Functions ◽

Interactive Environments ◽

Oculus Rift ◽

Non Invasive ◽

Brain Signals

The application of electroencephalography electrodes in Virtual Reality (VR) glasses allows users to relate cognitive, emotional, and social functions with the exposure to certain stimuli. The development of non-invasive portable devices, coupled with VR, allows for the collection of electroencephalographic data. One of the devices that embraced this new trend is Looxid LinkTM, a system that adds electroencephalography to HTC VIVETM, VIVE ProTM, VIVE Pro EyeTM, or Oculus Rift STM glasses to create interactive environments using brain signals. This work analyzes the possibility of using the Looxid LinkTM device to perceive, evaluate and monitor the emotions of users exposed to VR.

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