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EEG

Transcript: During - Electrodes are placed on the patient's scalp to detect electrical impulses within the brain - Normal findings are normal frequency, amplitude and characteristics of brain waves - Abnormal findings: seizures, brain tumor, brain abscess, intracranial hemorrhage, cerebral infarct, cerebral death, encephalitis, narcolepsy (detects sleep waves during normal waking hours), metabolic encephalopathy Overview Teaching Electroencephalography Highlights After - Instruct the patient to wash his hair the night before the test. Patient should not use any oils or sprays - Instruct patient not to drink any coffee, tea, cocoa, or cola the morning before the test - Instruct patient he will need to remain still during the test - Instruct the patient that he should not fast before the test. (Pt should eat to avoid hypoglycemia) *to identify brain function abnormalities * Due to the EEG's ability to evaluate the overall electrical activity of the brain, it can be used to determine trauma and cerebral death - Help the patient to remove the electrode paste with acetone - Instruct and help patient shampoo hair - Instruct patient who has had a sleep EEG not to drive home alone - Ensure safety precautions until the effects of any sedatives have worn off if sleep EEG indicated Before Complications/contraindication/ interfering factors * to identify and evaluate patients with seizures *identify conditions involving the brain cortex *tumors *infarction *abscess - Procedure performed by an EEG technician so nurse is generally not present for procedure - Help the patient to lie in a supine position - Patient is asked to hyperventilate by breathing deeply 20 times per minute for 3 minutes - Light may be flashed over the patient with the patient's eyes opened or closed - Electrodes will cover prefrontal, frontal, temporal, parietal, occipital areas of the skull - Patient may be asked to hyperventilate by breathing deeply 20 times per minute for 3 minutes - Light may be flashed over the patient with the patient's eyes closed or opened (to detect light-stimulated seizures) Procedure and Findings Purpose - Explain procedure to patient - Assure patient that the test cannot read minds or test senility - Assure patient that he will not feel anything during the procedure - Instruct the patient to wash his hair the night before the test. Patient should not use any oils or sprays - Check with physician if any medications need to be discontinued - Instruct patient if sleeping time should be shortened the night before the test - Instruct patient not to fast before study - Instruct patient not to drink any coffee, tea, cocoa, or cola the morning before the test - Instruct patient he will need to remain still during the test - Fasting causing hypoglycemia - Drinks containing caffeine - Movement during the test - Light can alter results - Drugs such as sedatives - Seizure can be induced due to the hyperventilation and flashing lights - EEG is primarily used for identifying epilepsy, but can also identify cerebral death as well as be used in a craniotomy - Be sure to instruct the patient to wash his hair the night before and not to use oils or lotions - Be sure to instruct the patient to eat before the test to avoid hypoglycemic reaction -Be sure to instruct patient to avoid caffeinated beverages EEG

EEG

Transcript: Electroencephlocardiograms used to measure effects http://www.webmd.com/epilepsy/electroencephalogram-eeg-21508 * 1980's- invention of computers allowed EEG brain topography to develop http://www.cerebromente.org.br/n03/tecnologia/historia.htm Future * The patient's head is covered in a gel to improve the conduction from the skull to the electrodes. It is important for the patients hair to be clean so there is little to no interference. * Many electrodes on placed on the patients skull, and a computer records the response. * The patient is usually sleep deprived and sometimes subject to flashing lights and hyperventilation. This is done because usually abnormalities are most apparent when the brain is under stress. Spectoral analysis 3-D brain reconstructions * There are very little risks because it measures brain waves on the surface of the scalp. * It can be riskier for people with seizure disorders because of the flashing lights and hyperventilating techniques used during this procedure, which can trigger a seizure. How is this procedure performed? Uses in medicine * 1875-Richard Canton discovers the EEG from the exposed brains of rabbits and monkies * Can detect whether or not a person is brain dead or just in a coma. * Effective in detecting and diagnosing the biological aspects of psychological and neurodegnerative disorders. * Treatements with an EEG can monitor the frequency of brain wave changes in children with ADHD and normalize them. * EEG biofeedback can be used improve attitude and emotional balance in people with learning disabilities, migrines, chronic pain, and teeth grinding. * Neurobiofeedback gives a person better control of biological and phsyiological functions. * EEG stimulation has been proven to improve memory and accelerate learning. Also, it improves sleeping patterns in people who have sleeping difficulties. Sources * Because EEG's measure action potentials (electic potentials), they are very similar to a voltmeter. Waves of ions reach the scalp, and affect the electrons on the metal electrodes. This difference is measured by the voltmeter and recorded over time. Risks involved in EEG tests *Advancement of computers has led to many great things http://bml.ym.edu.tw/ibs/brain/curricurium/952curricurium/file/MEG_EEG_Clinical%20applications.pdf * Because it measure electrical activity, it is not suggested for recording all types of brain activity. It is usually used to detect: http://www.nlm.nih.gov/medlineplus/ency/article/003931.htm http://benefitof.net/benefits-of-eeg/ http://www.edonline.com/collegecompass/oohb0114.htm http://www.medicalengineer.co.uk/pages/medical-imaging/eeg-applications-in-modern-medicine.html Physics Principles Electrode patch wires are placed on designated spots on the head. These electrods record four types of brainwaves according to frequencies: alpha, beta, theta, and delta. EEG technicians study the recorded changes in the brain impulses intently and then proceed with diagnosing the patient. A single axon potential is too small to be picked up, so EEG's actually measure the summation of thousands or millions of neurons synchronously firing. Who is this test for? History History *1957- W Gray Walter invented the toposcope- a series of cathode ray tubes attached to electrodes attached to the skull. Biofeedback (neurofeedback) can be used to diagnose and treat patients. Biofeedback is the information provided from an EEG. EEG's from a physics perspective How it works * Tumors * Focal disease epilepsy arterivenous mal-formations stroke * Disturbances in consciousness and viligance narcolepsy coma * Effects of withdrawal from psychoactive drugs * Effects of infectious diseases Braim Meningites * Identifying a psychological disorder with a physical cause schizophrenia dementias hyperactivity depression brain atrophy attention deficit disorders Used any time the electical potential in the brain needs to be monitered http://en.wikipedia.org/wiki/Electroencephalography Improvment of life * 1929- Hans Berger measured the human brains electrical activity on the scalp

EEG

Transcript: Classification of hand movements from Eeg signals Presented By: Name: Pranali Kokate ID: 2019PEB5431 Supervisor: Dr. Amit Joshi Introduction Electroencephalogram (EEG) records electrical signals from the brain, thus providing the ability to extract valuable information regarding brain activity. [1] Brain computer Interface(BCI) can also help commnunicate brain commands and enable the control of artifical limbs [2], espcially for people suffering from brainstem stroke, brain or spinal injury. Introduction [1]Guangyi Zhang, Ali Etemad, A.S.Moghaddam, Y Zhang, "Classification of Hand Movements from EEG Using a Deep Attention based LSTM Network." IEEE Senors Journal ,March 15 2020. [2] S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, “Brain computer interfacing: Applications and challenges,” Egyptian Inform. J., vol. 16,pp. 213–230, Jul. 2015 literature review literature Survey Research gap Research gap Proper selection of channels. Despite applying the digital filter to EEG, contaminated data still exists, because such data is generated by eye blinks and head movements. To obtain cleaned EEG data, we removed the contamination factors by an independent component analysis (ICA) which is commonly used to decompose the brain signals into statistically independent components (ICs). [3] Developing a robust Acquisition model. [3] Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals, Ji-Hoon Jeong , Kyung-Hwan Shim , Dong-Joo Kim , and Seong-Whan Lee , 5, May 2020 Objective 1. To study and understand the EEG signals using MNE-Python package. [4] Phase I 2. To design a model to classify the daliy activites from EEG signals. Phase II [4] https://mne.tools/dev/auto_tutorials/raw/plot_30_annotate_raw.html#sphx-glr-auto-tutorials-raw-plot-30-annotate-raw-py Methodology Methodology 10 central sensors were discarded due to non symmetric nature[5]. Two filters were used a notch filter (50Hz) to remove powerline interference.[6] Bandpass filter having Lowpass frequency: 0.5Hz and Highpass frequency: 70Hz.[1] Normalization of EEG Amplitude using min-max Normalization[7][1] [1] Guangyi Zhang, Ali Etemad, A.S.Moghaddam, Y Zhang, "Classification of Hand Movements from EEG Using a Deep Attention based LSTM Network." IEEE Senors Journal ,March 15 2020. [5] Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, EEG-Based Emotion Recognition in Music Listening, 2010 [6] Ji-Hoon Jeong , Kyung-Hwan Shim , Dong-Joo Kim , and Seong-Whan Lee , "Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals" May, 2020 [7] https://en.wikipedia.org/wiki/Feature_scaling data-set BCI Dataset for motor imagery and motor execution The EEG Movement Dataset was used in this study. The dataset includes 109 subjects and has been collected using a BCI 2000 system. Participants were asked to perform three actions: rest (T0), left hand movement (T1), and right hand movement (T2). Each experiment consisted of 15 iterations, where T0 was followed by a visual stimulus, randomly selecting either T1 or T2. This 15-pair movement process was repeated 3 times. Accordingly, the dataset contains a total of 103 subjects × 3 experiments × 15 movements, for a total of 4635 movements. The dataset contains 64-channels of EEG, recorded at a sampling frequency of 160 Hz. Figure 3 illustrates a sample EEG recording and the three actions T0,T1, and T2.[7] [7] https://www.physionet.org/content/eegmmidb/1.0.0/ T0 corresponds to rest T1 corresponds to onset of motion (real or imagined) of the left fist (in runs 3, 4, 7, 8, 11, and 12) T2 corresponds to onset of motion (real or imagined) of the right fist (in runs 3, 4, 7, 8, 11, and 12) time Domain Features Time Domain Features-Phase I The signal acquisition is done at the rate of 160 samples per second. Here we would be using 2 sec window for extracting features. Following are the features which we would use for training: [1][7] Moving window averaging [8] Variance [8] Skewness [8] Kurtosis [8] Zero-crossing- rate [9] Absolute area under signal [8] Peak to Peak distance [8] Feature ranking was done by random forest here mean, skewness and area under the curve was ranked highest among all the features According to class separability two features ZCR and Peak to Peak distance were discarded since their separability came below 0.5. [7] Sidharth Pancholi and Amit M. Joshi, "Time Derivative Moments Based Feature Extraction Approach for Recognition of Upper Limb Motions Using EMG" 4, APRIL 2019 [8] Numpy library and scipy library. [9] Features for Content-Based Audio Retrieval- why zero-crossing is important in signals. frequency Domain Features frquency domain features Phase-II Amplitude spectrum Density [10] Power spectral density Power of Each frequency Band Extracted frequency-domain features consisted of relative band power in four frequency bands, notably i) delta (0.5 − 4 Hz), ii) theta (4 − 8 Hz), iii) alpha (8 − 12 Hz),

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