A brain-computer interface (BCI) is a technology that acquires and analyzes electrical signals from the brain to control external devices. BCI technologies can generally be used to control a computer cursor, limb orthosis, or word processing. This technology can also be used as a neurological rehabilitation tool for people with poor motor control. We reviewed historical attempts and methods toward predicting arm movements using brain waves. In addition, representative studies of minimally invasive and noninvasive BCI were summarized.

Citations

• Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank
  Z. T. Al-qaysi, A. S. Albahri, M. A. Ahmed, Mahmood M. Salih
  Neural Computing and Applications.2024; 36(17): 10355.     CrossRef

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer’s disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

Citations

• Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network
  Geqi Qi, Rui Liu, Wei Guan, Ailing Huang
  Cyborg and Bionic Systems.2024;[Epub]     CrossRef

Hashimoto’s encephalopathy (HE) is a heterogeneous encephalopathy with diverse clinical presentations. Here we report on a 69-year-old woman who presented with confusion, aphasia, fever, and focal ictal discharges. Cerebrospinal fluid analysis and a workup for other fever origins revealed no abnormality and a high level of thyroperoxidase antibody was detected, which findings led to a diagnosis of HE. The symptoms subsided after treatment. This study highlights the importance of considering HE in patients presenting with fever and abnormal EEG findings.

Electroencephalography (EEG) is frequently used to assist the diagnosis of brain death. However, to date there have been no guidelines in terms of EEG criteria for determining brain death in Korea, despite EEG being mandatory. The purpose of this review is to provide an update on the evidence and controversies with regarding to the utilization of EEG for determining brain death and to serve as a cornerstone for the development of future guidelines. To determine brain death, electrocerebral inactivity (ECI) should be demonstrated on EEG at a sensitivity of 2 μV/mm using double-distance electrodes spaced 10 centimeters or more apart from each other for at least 30 minutes, with intense somatosensory or audiovisual stimuli. ECI should be also verified by checking the integrity of the system. Additional monitoring is needed if extracerebral potentials cannot be eliminated. Interpreting EEG at high sensitivities, which is required for the diagnosis of brain death, can pose a diagnostic challenge. Furthermore, EEG is affected by physiologic variables and drugs. However, no consensus exists as to the minimal requirements for blood pressure, oxygen saturation, and body temperature during the EEG recording itself, the minimal time for observation after the brain injury or rewarming from hypothermia, and how to determine brain death when the findings of ECI is equivocal. Therefore, there is a strong need to establish detailed guidelines for performing EEG to determine brain death.

Citations

• 3D EEG and Clinical Evidence of Brain Dying. Preliminary Report
  M. Drobný, B. Drobná Sániová, S. Učňová, G. Sobolová, R. Koyš, C. Machado, Ya. Machado
  General Reanimatology.2023; 19(1): 34.     CrossRef
• Brain Death and Its Prediction in Out-of-Hospital Cardiac Arrest Patients Treated with Targeted Temperature Management
  Hwan Song, Sang Hoon Oh, Hye Rim Woo
  Diagnostics.2022; 12(5): 1190.     CrossRef
• MRI of the brain, CT of the chest and abdomen on the 1^st and 7^th day after a clinical death (clinical case, literature review)
  T. N. Trofimova, A. D. Khalikov, S. N. Pirgulov
  Diagnostic radiology and radiotherapy.2021; 12(3): 101.     CrossRef
• Determination of Brain Death/Death by Neurologic Criteria
  David M. Greer, Sam D. Shemie, Ariane Lewis, Sylvia Torrance, Panayiotis Varelas, Fernando D. Goldenberg, James L. Bernat, Michael Souter, Mehmet Akif Topcuoglu, Anne W. Alexandrov, Marie Baldisseri, Thomas Bleck, Giuseppe Citerio, Rosanne Dawson, Arnold
  JAMA.2020; 324(11): 1078.     CrossRef
• A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features
  Li Zhu, Gaochao Cui, Jianting Cao, Andrzej Cichocki, Jianhai Zhang, Changle Zhou
  Sensors.2019; 19(6): 1342.     CrossRef

The performance of electroencephalogram (EEG) recordings is affected by electrode type, electronic parameters such as filtering, amplification, signal conversion, data storage; and environmental conditions. However, no single method has been identified for optimal EEG recording quality in all situations. Therefore, we aimed to provide general principles for EEG electrode selection as well as electronic noise reduction, and to present comprehensive information regarding the acquisition of satisfactory EEG signals. The standards provided in this document may be regarded as Korean guidelines for the clinical recording of EEG data. The equipment, types and nomenclature of electrodes, and the details for EEG recording are discussed.

Citations

• Basics of Electroencephalography for Neuropsychiatrist
  Hun Jeong Eun
  Journal of Korean Neuropsychiatric Association.2019; 58(2): 76.     CrossRef

Background
: We studied EEG changes during pilocarpine-induced status epilepticus(SE), a widely used model whose EEG characteristics have not been fully described previously. Method : Male Sprague-Dawley rats weighing 250-350 grams were used as subjects. SE was induced 5-7 days after placement of chronic epidural electrodes, using 360-380mg/Kg pilocarpine IP. Rats were observed with continuous EEG recording following pilocarpine injection until end of the SE episode. Results : SE occurred in 10/12 rats studied. SE began with a series of discrete seizures 11.1?.93 minutes after pilocarpine injection. 5.2?.71 seizures occurred over 10.9?.62 minutes, until the EEG converted to a waxing and waning pattern, during which the amplitude and frequency of epileptiform activity increased. After 1.4?.82 minutes, a pattern of continuous high amplitude rapid spiking was established. Continuous spiking continued for 3.4?.48 hours with a very gradual decline in amplitude and frequency, until periodic epileptiform discharges(PEDs) began to occur. The EEG consisted primarily of PEDs for another 7.4?.09 hours, until electrographic generalized seizures beganto occur. These continued for 5.8?.82 hours until death. Duration of SE was 17.0?.88 hours. Flat periods were a prominent feature during all EEG patterns in this model. Conclusion : EEG features distinctive in pilocarpine SE(but not unique to it) include flat periods during all patterns and resumption of continuous spiking episodes after the onset of PEDs. The sequence of discrete seizures to waxing and waning to continuous spiking to PEDs was identical to that which has been described in humans and other animal models.

Electroencephalography(EEG) involves the recording and analysis of electrical signals generated by brain. Resolution of true electrical brain activity requires three elements: good equipment, meticulous recording technique, and informed interpretation of data. Every electroencephalographer should be familiar with the science and engineering underlying clinical EEG. This article reviews principle of EEG instrument & methods of recording; History of EEG, EEG instrument, EEG amplifier & its control, Calibration, Electrode, Electrode placement, Montage, and Electrical safety.

Artifacts in an electroencephalography are the signals not originated in the cerebrum. Generally, the artifacts are categorized into physiological artifacts and nonphysiological artifacts. An artifact created in the generator existing in the body but not originated in the brain is physiological and an artifact occurred by various causes existing outside of the body is nonphysiological. The physiological artifacts are occurred by eyeball movement, electrocardiogram, tongue movement, skin potential and body movement, and the nonphysiological artifacts by instrument, electrode, environment and digital signal defect. To recognize and eliminate the artifacts caused by various origins is responsibility of the EEG technician primarily. However even an EEG technician abundant in experiences cannot remove all artifacts. The EEG technician must give information about the artifacts which he/she was not able to remove after endeavoring to the EEG interpreter. The responsibility about the quality control of EEG is fundamentally on the EEG interpreter. The EEG interpreter must accumulate the knowledge about artifacts abundantly to advise the EEG technician to correct frequently occurred artifacts and to determine the artifacts recorded in the EEG correctly.

Periodic lateralized epileptiform discharges(PLEDs) are usually seen in acute and subacute cerebral lesions. Occasionally PLEDs could be observed in persistent structural lesions. We observed PLEDs-plus in a patient with right basal ganglionic hemorrhage, at 10 months and 13 months after the stroke. The patients suffered two seizures 3 months and 5 days before recording of EEG. PLEDs-plus may persist as an interictal abnormal finding and the rhythmic dischargeof that may be increased by a seizure.

Intraoperative neuromonitoring (INM) is well known to be useful method to reduce intraoperative complications during the surgery of nervous system lesions. Evoked potentials are most commonly used among the electrophysiological tests. Brainstem auditory evoked potentials are for detecting the problems along the auditory pathways including the eighth cranial nerve and brainstem. Somatosensory evoked potentials are applied for preventing the spinal cord lesions. The INM is affected by many factors. In order to perform an optimal INM, the confounding factors including technical, anesthetical, and individual factors should be kept well under control. INM has frequent electrophysiologic changes during the surgery and it might be helpful to keep one

Even though the origin and nature of nocturnal paroxysmal dystonia (NPD) remains unclear, it has been considered as a manifestation of the nocturnal frontal lobe epilepsy. We report a 17-year-old man with abnormal stereotyped movement during sleep. Video-EEG monitoring, ictal SPECT and night polysomnography did not show any evidence of epilepsy. However, the partial response to large dose of carbamazepine and the scoring according to the frontal lobe epilepsy and parasomnias (FLEP) scale suggest his events could be classified as epilepsy. Therefore we think the FLEP scale might be a useful tool for differential diagnosis in a patient presenting NPD.

We present a recordings of 37-year-old woman with simultaneous ictal scalp and subdural electrodes. The ictal rhythmon subdural electrocorticography (ECoG) started earlier (median 24.5 sec) and ended later (median 2.0 sec) compared toictal rhythm on scalp EEG. Eight ictal ECoG recordings were well localized to left temporal area, whereas ictal scalp EEGrecordings were not. Our case shows the obvious timing relations between two recordings, and different electrophysiologicinformation about localization of ictal onset zone.