scholarly journals Voltage Distributions in Extracellular Brain Recordings

2021 ◽  
Author(s):  
Nicholas Vaughan Swindale ◽  
Peter Rowat ◽  
Matthew R Krause ◽  
Martin A Spacek ◽  
Catalin C Mitelut
Keyword(s):  
Action Potentials ◽  
Field Potential ◽  
Sleep Stages ◽  
Time Varying ◽  
Eeg Signals ◽  
Extracellular Recordings ◽  
Eeg Recordings ◽  
Log Linear ◽  
High Pass ◽  
Voltage Distributions

Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time-varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus and hippocampus, in monkeys, cats and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ± 1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, i.e. they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behaviour of ion channels which produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals.

Electroencephalography and evoked potentials: a PC-based analysis program for laboratory courses in physiology.

1992 ◽  
Vol 263 (6) ◽  
pp. S16
Author(s):  
M Illert ◽  
H Wiese ◽  
U Wolfram
Keyword(s):  
Evoked Potentials ◽  
Epileptic Seizures ◽  
Dental Students ◽  
Sleep Stages ◽  
Data Sets ◽  
Eeg Signals ◽  
Cerebral Activity ◽  
Laboratory Courses ◽  
Mental Activation ◽  
Eeg Recordings

A computer program (EEG Analysis) was developed for the preclinical laboratory course in physiology held for medical and dental students. It offers an off-line analysis of a set of typical and frequently occurring physiological and pathological electroencephalogram (EEG) and evoked potential (EP) recordings, which are stored in an IBM-compatible personal computer (PC) system. The users are requested to measure and analyze the data sets and to work through a base of questions relevant in the frame of the particular topic. The program is structured in several exercises: calibration, pickup of non-EEG signals (eye movements, chewing), waveforms in EEG recordings from awake subjects (alpha-waves, beta-waves), desynchronization of cerebral activity (visual activation, acoustic activation, mental activation), habituation of cerebral activity upon acoustic stimuli, EEG recordings from asleep subjects (different sleep stages, sleep-specific EEG signals), epileptic seizures, and EPs (principle of averaging, visually evoked potentials in different cortical areas). The program runs under MS-DOS and is network capable. The software structure ensures maximal flexibility for rapid changes and adaptations of the program to specific needs of a particular EEG course. The program has been used for three years, and the response from > 800 students has been consistently positive.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

2020 ◽  
Author(s):  
Antonio Quintero Rincón ◽  
Hadj Batatia ◽  
Jorge Prende ◽  
Valeria Muro ◽  
Carlos D'Giano
Keyword(s):  
Statistical Model ◽  
Human Subjects ◽  
True Positive Rate ◽  
Wave Pattern ◽  
Absence Epilepsy ◽  
Patient Specific ◽  
Eeg Signals ◽  
True Negative ◽  
Eeg Recordings

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

scholarly journals Detection of Heart Disease Risk Utilizing Correlation Matrix, Random Forest and Permutation Feature Importance Approaches

2020 ◽  
pp. 29-34
Author(s):  
Sude Pehlivan ◽  
Yalcin Isler
Keyword(s):  
Disease Risk ◽  
Wavelet Packet ◽  
Eeg Signals ◽  
Eyes Closed ◽  
The People ◽  
Low Pass ◽  
Eyes Open ◽  
Surface Eeg ◽  
Heart Disease Risk ◽  
High Pass

Surface EEG measurements that can be performed in hospitals and laboratories have reached a wearable and portable level with the development of today's technologies. Artificial intelligence-assisted brain-computer interface (BCI) systems play an important role in individuals with disabilities to process EEG signals and interact with the outside world. In particular, the research is becoming widespread to meet the basic needs of individuals in need of home care with an increasing population. In this study, it is aimed to design the BCI system that will detect the hunger and satiety status of the people on the computer platform through EEG measurements. In this context, a database was created by recording EEG signals with eyes open and eyes closed by 20 healthy participants in the first stage of the study. The noise of the EEG signal is eliminated by using a low pass, high pass, and notch filters. In the classification, using Wavelet Packet Transform (WPT) with Coiflet 1 and Daubechies 4 wavelets, 77.50% accuracy was achieved in eyes closed measurement, and 81% in eyes open measurement.

Epileptiform activity induced by low chloride medium in the CA1 subfield of the hippocampal slice

1990 ◽  
Vol 64 (6) ◽  
pp. 1747-1757 ◽  
Author(s):  
M. Avoli ◽  
C. Drapeau ◽  
P. Perreault ◽  
J. Louvel ◽  
R. Pumain
Keyword(s):  
Membrane Potential ◽  
Large Amplitude ◽  
Action Potentials ◽  
Hippocampal Slice ◽  
Epileptiform Activity ◽  
Field Potential ◽  
Pyramidal Cells ◽  
Input Resistance ◽  
Field Potentials ◽  
Maximal Amplitude

1. Extracellular and intracellular recordings and measurements of the extracellular concentration of free K+ ([K+]o) were performed in the CA1 subfield of the rat hippocampal slice during perfusion with artificial cerebrospinal fluid (ACSF) in which NaCl had been replaced with equimolar Na-isethionate or Na-methylsulfate (hereafter called low Cl- ACSF). 2. CAl pyramidal cells perfused with low Cl- ACSF generated intracellular epileptiform potentials in response to orthodromic, single-shock stimuli delivered in stratum (S.) radiatum. Low-intensity stimuli evoked a short-lasting epileptiform burst (SB) of action potentials that lasted 40–150 ms and was followed by a prolonged hyperpolarization. When the stimulus strength was increased, a long-lasting epileptiform burst (LB) appeared; it had a duration of 4–15 s and consisted of an early discharge of action potentials similar to the SB, followed by a prolonged, large-amplitude depolarizing plateau. The refractory period of the LB was longer than 20 s. SB and LB were also seen after stimulation of the alveus. 3. Variations of the membrane potential with injection of steady. DC current modified the shape of SB and LB. When microelectrodes filled with the lidocaine derivative QX-314 were used, the amplitudes of both SB and LB increased in a linear fashion during changes of the baseline membrane potential in the hyperpolarizing direction. The membrane input resistance, as measured by injecting brief square pulses of hyperpolarizing current, decreased by 65-80% during the long-lasting depolarizing plateau of LB. 4. A synchronous field potential and a transient increase in [K+]o accompanied the epileptiform responses. The extracellular counterpart of the SB was a burst of three to six population spikes and a small increase in [K+]o (less than or equal to 2 mM from a resting value of approximately 2.5 mM). The LB was associated with a large-amplitude, biphasic, negative field potential and a large increase in [K+]o (up to 12.4 mM above the resting value). Changes in [K+]o during the LB were largest at the border between S. oriens and S. pyramidale. This was also the site where the field potentials measured 2–5 s after the stimulus attained their maximal amplitude. Conversely, field potentials associated with the early component of the LB or with the SB displayed a maximal amplitude in the S. radiatum. 5. Spontaneous SBs and LBs were at times recorded in the CA1 and in the CA3 subfield.(ABSTRACT TRUNCATED AT 400 WORDS)

On the synchronous activity induced by 4-aminopyridine in the CA3 subfield of juvenile rat hippocampus

1993 ◽  
Vol 70 (3) ◽  
pp. 1018-1029 ◽  
Author(s):  
M. Avoli ◽  
C. Psarropoulou ◽  
V. Tancredi ◽  
Y. Fueta
Keyword(s):  
Nmda Receptor ◽  
Gabaa Receptor ◽  
Action Potentials ◽  
Hippocampal Slices ◽  
Field Potential ◽  
Pyramidal Cells ◽  
Occurrence Rate ◽  
Gamma Aminobutyric Acid ◽  
Synchronous Activity ◽  
Ca3 Pyramidal Cells

1. Extracellular field potential and intracellular recordings were made in the CA3 subfield of hippocampal slices obtained from 10- to 24-day-old rats during perfusion with artificial cerebrospinal fluid (ACSF) containing the convulsant 4-aminopyridine (4-AP, 50 microM). 2. Three types of spontaneous, synchronous activity were recorded in the presence of 4-AP by employing extracellular microelectrodes positioned in the CA3 stratum (s.) radiatum: first, inter-ictal-like discharges that lasted 0.2-1.2 s and had an occurrence rate of 0.3-1.3 Hz; second, ictal-like events (duration: 3-40 s) that occurred at 4-38 x 10(-3) Hz; and third, large-amplitude (up to 8 mV) negative-going potentials that preceded the onset of the ictal-like events and thus appeared to initiate them. 3. None of these synchronous activities was consistently modified by addition of antagonists of the N-methyl-D-aspartate (NMDA) receptor to the ACSF. In contrast, the non-NMDA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX, 2-10 microM) reversibly blocked interictal- and ictallike discharges. The only synchronous, spontaneous activity recorded in this type of medium consisted of the negative-going potentials that were abolished by the GABAA receptor antagonists bicuculline methiodide (5-20 microM) or picrotoxin (50 microM). Hence they were mediated through the activation of the GABAA receptor. 4. Profile analysis of the 4-AP-induced synchronous activity revealed that the gamma-aminobutyric acid (GABA)-mediated field potential had maximal negative amplitude in s. lacunosum-moleculare, attained equipotentiality at the border between s. radiatum and s. pyramidale, and became positive-going in s. oriens. These findings indicated that the GABA-mediated field potential presumably represented a depolarization occurring in the dendrites of CA3 pyramidal cells. 5. This conclusion was supported by intracellular analysis of the 4-AP-induced activity. The GABA-mediated potential was reflected by a depolarization of the membrane of CA3 pyramidal cells that triggered a few variable-amplitude, fractionated spikes or fast action potentials. By contrast, the ictal-like discharge was associated with a prolonged depolarization during which repetitive bursts of action potentials occurred. Short-lasting depolarizations with bursts of action potentials occurred during each interictal-like discharge. 6. The GABA-mediated potential recorded intracellularly in the presence of CNQX consisted of a prolonged depolarization (up to 12 s) that was still capable of triggering a few fast action potentials and/or fractionated spikes.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals A Novel Adaptive Fading Kalman Filter-Based Approach to Time-Varying Brain Spectral/Connectivity Analyses of Event-Related EEG Signals

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 51230-51245
Author(s):  
Jiewei Li ◽  
Shing-Chow Chan ◽  
Zhong Liu ◽  
Chunqi Chang
Keyword(s):  
Kalman Filter ◽  
Time Varying ◽  
Eeg Signals

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.

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