Brain Electrical Activity Research Articles

Exploring the potential of combining transcranial magnetic stimulation and electroencephalography to investigate mild cognitive impairment and Alzheimer's disease: a systematic review.

Transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are non-invasive techniques used for neuromodulation and recording brain electrical activity, respectively. The integration of TMS-EEG has emerged as a valuable tool for investigating the complex mechanisms involved in age-related disorders, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). By systematically synthesizing TMS-EEG studies, this review aims to shed light on the neurophysiological mechanisms underlying MCI and AD, while also exploring the practical applications of TMS-EEG in clinical settings. PubMed, ScienceDirect, and PsychInfo were selected as the databases for this review. The 22 eligible studies included a total of 592 individuals with MCI or AD as well as 301 cognitively normal adults. TMS-EEG assessments unveiled specific patterns of corticospinal excitability, plasticity, and brain connectivity that distinguished individuals on the AD spectrum from cognitively normal older adults. Moreover, the TMS-induced EEG features were observed to be correlated with cognitive performance and the presence of AD pathological biomarkers. The comprehensive examination of the existing studies demonstrates that the combination of TMS and EEG has yielded valuable insights into the neurophysiology of MCI and AD. This integration shows great potential for early detection, monitoring disease progression, and anticipating response to treatment. Future research is of paramount importance to delve into the potential utilization of TMS-EEG for treatment optimization in individuals with MCI and AD.

Influence of High-frequency Yoga Breathing (Kapalabhati) on States Changes in Gamma Oscillation.

Yoga breathing has been shown to enhance neurocognitive function and positive emotions by increasing electrical power in several frequency bands and synchronizing interhemispheric brain waves. The current study examined the immediate impact of practicing Kapalabhati (KBH) on the electrical activity of the brain. Thirty-six individuals who met the inclusion and exclusion criteria and ranged in age from 18 to 25 were randomly assigned, 1:1, to the KBH (n = 18) and breath awareness (BAW) (n = 18) groups. Before data collection, both groups received their respective practices for 10 min each day for a total of 15 days. The brain's electrical activities were assessed using 128-channel EEG recording. The electrodes were placed on their scalps according to the international 10-10 system, ensuring optimal coverage of different brain regions. The EEG signals were amplified, digitized, and stored for offline analysis. The EEG data showed that the practice of KBH significantly increased alpha waves in the frontal and temporal regions. Moreover, gamma waves increased significantly in the frontal, temporal, and occipital regions after the practice of KBH when compared with BAW. The results suggest the involvement of frontal and temporal regions, which highlights the importance of KBH in enhancing higher-order cognitive processes. These results provide valuable insights and support for the use of KBH as a potential intervention for individuals seeking to enhance their cognitive abilities.

Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function

In this article, we performed a preliminary test with the mean square fluctuation (RMS) function, using EEG data, in which the task consisted of the response given to the real/imagined motor stimulus. All steps, up to the configuration of the RMS function, relied on information from subject S010, available in the Physionet database. We analyzed 12 of the 64 channels and four stimuli. We evaluated the amplitude of channels 32 and 37 in relation to the others (11, 22, 24, 43, 44, 49, 54, 61, 63 and 64). We quantitatively observed similarities when the brain perfoRMS the same real/imagined motor task, and that the timing of the amplitude changes with increasing n scale (time scales). In all experiments (S010R03, S010I03, S010R04, S010I04, S010R05, S010I05, S010R06 and S010I06), channels 32 x 22(∆logF32,22) and 37 x 24 (∆logF37,24), for n > 100 (60 seconds), were smaller than the others, showing so channels 32 and 37 (left/right hemisphere) have the greatest fluctuation and differences between them. From data processing (.EDF) to visualization of the curves (FDFA / ∆log), we concluded that it is possible to replicate the test with more channels and subjects, as well as investigate other types of activities of the human brain with signals extracted from the EEG.

Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom.

As traditional handwriting is progressively being replaced by digital devices, it is essential to investigate the implications for the human brain. Brain electrical activity was recorded in 36 university students as they were handwriting visually presented words using a digital pen and typewriting the words on a keyboard. Connectivity analyses were performed on EEG data recorded with a 256-channel sensor array. When writing by hand, brain connectivity patterns were far more elaborate than when typewriting on a keyboard, as shown by widespread theta/alpha connectivity coherence patterns between network hubs and nodes in parietal and central brain regions. Existing literature indicates that connectivity patterns in these brain areas and at such frequencies are crucial for memory formation and for encoding new information and, therefore, are beneficial for learning. Our findings suggest that the spatiotemporal pattern from visual and proprioceptive information obtained through the precisely controlled hand movements when using a pen, contribute extensively to the brain's connectivity patterns that promote learning. We urge that children, from an early age, must be exposed to handwriting activities in school to establish the neuronal connectivity patterns that provide the brain with optimal conditions for learning. Although it is vital to maintain handwriting practice at school, it is also important to keep up with continuously developing technological advances. Therefore, both teachers and students should be aware of which practice has the best learning effect in what context, for example when taking lecture notes or when writing an essay.

Evaluation of Brain Electrical Activity of Visual Working Memory with Time-Frequency Analysis.

The term visual working memory (VWM) refers to the temporary storage of visual information. In electrophysiological recordings during the change detection task which relates to VWM, contralateral negative slow activity was detected. It was found to occur during the information is kept in memory and it was called contralateral delay activity. In this study, the characteristics of electroencephalogram frequencies of the contralateral and ipsilateral responses in the retention phase of VWM were evaluated by using time-frequency analysis (discrete wavelet transform [DWT]) in the change detection task. Twenty-six volunteers participated in the study. Event-related brain potentials (ERPs) were examined, and then a time-frequency analysis was performed. A statistically significant difference between contralateral and ipsilateral responses was found in the ERP. DWT showed a statistically significant difference between contralateral and ipsilateral responses in the delta and theta frequency bands range. When volunteers were grouped as either high or low VWM capacity the time-frequency analysis between these groups revealed that high memory capacity groups have a significantly higher negative coefficient in alpha and beta frequency bands. This study showed that during the retention phase delta and theta bands may relate to visual memory retention and alpha and beta bands may reflect individual memory capacity.

Accurate Mental Stress Detection Using Sequential Backward Selection and Adaptive Synthetic Methods.

The daily experience of mental stress profoundly influences our health and work performance while concurrently triggering alterations in brain electrical activity. Electroencephalogram (EEG) is a widely adopted method for assessing cognitive and affective states. This study delves into the EEG correlates of stress and the potential use of resting EEG in evaluating stress levels. Over 13 weeks, our longitudinal study focuses on the real-life experiences of college students, collecting data from each of the 18 participants across multiple days in classroom settings. To tackle the complexity arising from the multitude of EEG features and the imbalance in data samples across stress levels, we use the sequential backward selection (SBS) method for feature selection and the adaptive synthetic (ADASYN) sampling algorithm for imbalanced data. Our findings unveil that delta and theta features account for approximately 50% of the selected features through the SBS process. In leave-one-out (LOO) cross-validation, the combination of band power and pair-wise coherence (COH) achieves a maximum balanced accuracy of 94.8% in stress-level detection for the above daily stress dataset. Notably, using ADASYN and borderline synthesized minority over-sampling technique (borderline-SMOTE) methods enhances model accuracy compared to the traditional SMOTE approach. These results provide valuable insights into using EEG signals for assessing stress levels in real-life scenarios, shedding light on potential strategies for managing stress more effectively.

Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.

The methodology for diagnosing and managing stress developed by Grivtsova, as an independent method and as part of the preparation for inducing lucid dreams within the framework of the “Bancheko Extended Algorithm,” and its application in the “Global Forecasting System,” serves as a means of transferring transcendental states of consciousness within the “Bancheko-Technology Set”

According to the World Health Organization (WHO), the increase in stressful situations due to the accelerating pace of life, environmental pollution, urbanization, technological progress, and low socio-economic living standards of the population, underpins the rise in neurological diseases. Consequently, one of the crucial tasks in practical psychology and applied psychophysiology is the timely and comprehensive diagnosis and correction of borderline forms of psychological disorders. This study examines the application of stress diagnosis and correction methodologies. The foundation is the wave model of brain electrical activity, observed using electroencephalography, based on an algorithm developed during the experiment. The key hypothesis of the study suggests that by following the developed self-organization system, based on performing simple actions, individuals with disrupted brain electrical activity due to various stressors can avoid pharmacological interventions. It has been shown that the consequences of stress primarily include increased tension in the central nervous system (sympathetic component) and insufficient relaxation (parasympathetic component). During the exacerbation of chronic diseases, the following effects were observed in subjects: poor sleep in patients with gastritis, drowsiness and headaches in individuals with allergies, chronic migraines, and neurological pains in the head on the left side along the trigeminal nerve, meteorological dependence in hypertensive patients, and jaw locking and right-side pain in those with traumatic brain injuries. Correction of these disorders took into account their stress-induced nature. A method for rehabilitating emotional-affective disorders in individuals is proposed to overcome stressful situations. The obtained data suggest that the proposed method of restorative alpha rhythm through neurotraining has high potential in therapeutic work for overcoming and correcting stress in people from various social groups, ages, and professions.

Sex-related differences in young binge drinkers on the neurophysiological response to stress in virtual reality.

Stress is one of the main environmental factors involved in the onset of different psychopathologies. In youth, stressful life events can trigger inappropriate and health-damaging behaviors, such as binge drinking. This behavior, in turn, can lead to long-lasting changes in the neurophysiological response to stress and the development of psychological disorders late in life, e.g., alcohol use disorder. Our aim was to analyze the pattern of neurophysiological responses triggered with the exposition to a stressful virtual environment in young binge drinkers. AUDIT-3 (third question from the full AUDIT) was used to detect binge drinking (BD) in our young sample (age 18-25 years). According to the score, participants were divided into control (CO) and BD group. Next, a standardized virtual reality (VR) scenario (Richie's Plank) was used for triggering the stress response while measuring the following neurophysiological variables: brain electrical activity by electroencephalogram (EEG) and cortisol levels through saliva samples both measurements registered before and after the stressful situation. Besides, heart rate (HR) with a pulsometer and electrodermal response (EDA) through electrodes placed on fingers were analyzed before, during and after the VR task. Regarding the behavior assessed during the VR task, BD group spent significantly less amount of time walking forward the table and a tendency toward more time walking backwards. There was no statistically significant difference between the BD and the CO group regarding time looking down, but when we controlled the variable sex, the BD women group displayed higher amount of time looking down than the rest of the groups. Neurophysiological measurements revealed that there was not any statistically significant difference between groups in any of the EEG registered measures, EDA response and cortisol levels. Sex-related differences were found in HR response to VR scenario, in which BD women displayed the highest peak of response to the stressor. Also, the change in heartbeat was higher in BD women than men. Unveiling the neurophysiological alterations associated with BD can help us to prevent and detect early onset of alcohol use disorder. Also, from our data we conclude that participants' sex can modulate some stress responses, especially when unhealthy behaviors such as BD are present. Nevertheless, the moment of registration of the neurophysiological variables respect to the stressor seems to be a crucial variable.

Unveiling the acute neurophysiological responses to strength training: An exploratory study on novices performing weightlifting bouts with different motor learning models.

Currently, there is limited evidence regarding various neurophysiological responses to strength exercise and the influence of the adopted practice schedule. This study aimed to assess the acute systemic effects of snatch training bouts, employing different motor learning models, on skill efficiency, electric brain activity (EEG), heart rate variability (HRV), and perceived exertion as well as mental demand in novices. In a within-subject design, sixteen highly active males (mean age: 23.13 ± 2.09 years) randomly performed snatch learning bouts consisting of 36 trials using repetitive learning (RL), contextual interference (blocked, CIb; and serial, CIs), and differential learning (DL) models. Spontaneous resting EEG and HRV activities were recorded at PRE and POST training bouts while measuring heart rate. Perceived exertion and mental demand were assessed immediately after, and barbell kinematics were recorded during three power snatch trials performed following the POST measurement. The results showed increases in alpha, beta, and gamma frequencies from pre- to post-training bouts in the majority of the tested brain regions (p values ranging from < 0.0001 to 0.02). The CIb model exhibited increased frequencies in more regions. Resting time domain HRV parameters were altered following the snatch bouts, with increased HR (p < 0.001) and decreased RR interval (p < 0.001), SDNN, and RMSSD (p values ranging from < 0.0001 to 0.02). DL showed more pronounced pulse-related changes (p = 0.01). Significant changes in HRV frequency domain parameters were observed, with a significant increase in LFn (p = 0.03) and a decrease in HFn (p = 0.001) registered only in the DL model. Elevated HR zones (> HR zone 3) were more dominant in the DL model during the snatch bouts (effect size = 0.5). Similarly, the DL model tended to exhibit higher perceived physical (effect size = 0.5) and mental exertions (effect size = 0.6). Despite the highest psycho-physiological response, the DL group showed one of the fewest significant EEG changes. There was no significant advantage of one learning model over the other in terms of technical efficiency. These findings offer preliminary support for the acute neurophysiological benefits of coordination-strength-based exercise in novices, particularly when employing a DL model. The advantages of combining EEG and HRV measurements for comprehensive monitoring and understanding of potential adaptations are also highlighted. However, further studies encompassing a broader range of coordination-strength-based exercises are warranted to corroborate these observations.

GANN: EEG-based Emotion Classification Using Context-Aware Gated Attention Neural Network

Electroencephalography (EEG) is a valuable, non-invasive method for monitoring the brain's electrical activity and assessing high-level cognitive processes, including emotions. Despite its benefits, interpreting EEG data is challenging, complex, and prone to human error. This study introduces an advanced machine learning model aimed at improving EEG-based emotion classification by fully utilizing the spatial and temporal characteristics of EEG channels. Our proposed model combines Gated Recurrent Units (GRU) and Attention Mechanisms, significantly enhancing feature learning and model generalization across diverse datasets. Achieving a classification accuracy of 94% on the EEG Brainwave Dataset, our model outperforms existing methods. This work not only refines emotion recognition techniques but also establishes a benchmark for future research in effectively utilizing EEG data.

Task-Driven Neurophysiological qEEG Baseline Performance Capabilities in Healthy, Uninjured Division-I College Athletes.

Athletic performance can be measured with a variety of clinical and functional assessment techniques. There is a need to better understand the relationship between the brain's electrical activity and the body's physiological performance capabilities in real-time while performing physical tasks related to sport. Orthopedic functional assessments used to monitor the neuroplastic properties of the central nervous system lack objectivity and/or pertinent functionality specific to sport. The ability to assess brain wave activity with physiological metrics during functional exercises associated with sport has proven to be difficult and impractical in real-time sport settings. Quantitative electroencephalography or qEEG brain mapping is a unique, real-time comprehensive assessment of brain electrical activity performed in combination with physiometrics which offers insight to neurophysiological brain-to-body function. Brain neuroplasticity has been associated with differences in musculoskeletal performance among athletes, however comparative real-time normal data to benchmark performance capabilities is limited. This prospective, descriptive case series evaluated performance of task-driven activities using an innovative neurophysiological assessment technique of qEEG monitored neurophysiological responses to establish a comparative benchmark of performance capabilities in healthy, uninjured Division-I athletes. Twenty-eight healthy uninjured females (n=11) and males (n=17) NCAA Division-I athletes participated in real-time neurophysiological assessment using a Bluetooth, wireless 21-channel dry EEG headset while performing functional activities. Uninjured athletes experienced standard and regulated fluctuations of brain wave activity in key performance indicators of attention, workload capacity and sensorimotor rhythm (SMR) asymmetries. qEEG neurophysiological real-time assessment concurrent with functional activities in uninjured, Division-I athletes may provide a performance capability benchmark. Real-time neurophysiological data can be used to monitor athletes' preparedness to participate in sport, rehabilitation progressions, assist in development of injury prevention programs, and return to play decisions. While this paper focuses on healthy, uninjured participants, results underscore the need to discen pre-injury benchmarks. 4.

Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies

This paper deals with the scientific research in a new area of human activity – neuromarketing. The development of new software for neuromarketing research based on modern methods and tools of artificial intelligence and data science (neural network technologies) using technologies of high-performance computing and parallel programming are considered in this paper. The results of experimental measurements of electrical activity of the consumer's brain based on electroencephalographic signals depending on various types of marketing stimuli are presented in this paper. The quality of the developed software for neuromarketing is evaluated on the basis of various quality metrics.

The Erratic History of Electroencephalography

The history of electroencephalography begins around 1850 with a supposition of Emil du Bois-Reymond concerning the transfer of information by electric currents over the nerve to the effector. Luigi Galvani in Bologna showed with his frog legs experiments 1786 indeed the existence of animal electricity. Richard Caton in Liverpool placed in 1875 as the first electrodes on the brains of rabbits and was able to record the electrical brain activity. Caton received little attention for his research and left with his animal experiment aversion the field. In contrast to Caton, Adolf Beck in Kraków, got a huge attention for his recordings of the electrical activities of the frog brain. The most interesting response to his important paper of 1890 came from Vasili Danilewski from Charkov, mentioning his unpublished doctoral thesis of 1877. He did his dog recordings before Beck, but one year after Caton who send him his abstract, remaining that he discovered before him electric brain waves. Colleagues of Danilewski such as Práwdicz- Neminski from Kyiv performed also adequate work in this field, but due to Stalin's dogmatic regime, electrophysiology became prohibited and disappeared in Russian. Jewish Adolf Beck, now professor in Lwów, experienced there in war time in 1940 dangerous times and ultimately the Nazi’s came for him. His son succeeded to hand him a cyanide capsule to save him from the gas chamber. By the tragedies of the war in Poland and the split between East and West Europe, Beck and his work go lost. Hans Berger began in Jena with brain recordings in humans and in 1927 he exclaimed ‘I can record brain actvity from an intact human skull’. This was replicated abd agreed upon by Edgar Adrian in 1934, and Berger received international fame, but not in Germany. The often authoritative Berger hated the Nazi’s and was suspended by the regime. He came in a severe depression and ended his life by hanging in June 1941. In this heavy war time, electrophysiology moved to the USA. Frederic and Erna Gibbs at Harvard got, with their studies of epilepsy in 1934 a breakthrough in clinical electroencephalography, Alfred Loomis and Herbert Jasper used the electrical brain actvity to identify several stages of sleep. Later, Jasper performed in Montreal, with neurosurgeon Wilder Penfield, important research on focal epilepsies. After the second world war, around 1950, electroencephalography blossomed up, in the USA as well as in Europe. Recording electroencephalograms for studying the functioning of the brain and in particular investigating domains of sleep and epilepsy. It became common that almost every university and hospital had a resording device. That electroencephalography was a mature methodology, was demonstrated by great discoveries for example through the finding of REM sleep by Kleitman and Aserinski in 1953.

Electroencephalography: features of the obtained data and its applicability in psychiatry

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

Assessment of TENS-Evoked Tactile Sensations for Transradial Amputees via EEG Investigation.

Most of current prostheses can offer motor function restoration for limb amputees but usually lack natural and intuitive sensory feedback. Many studies have demonstrated that Transcutaneous Electrical Nerve Stimulation (TENS) is promising in non-invasive sensation evoking for amputees. However, the objective evaluation and mechanism analysis on sensation feedback are still limited. This work utilized multi-channel TENS with diverse stimulus patterns to evoke sensations on four non-disabled subjects and two transradial amputees. Meanwhile, electroencephalogram (EEG) was collected to objectively assess the evoked sensations, where event-related potentials (ERPs), brain electrical activity maps (BEAMs), and functional connectivity (FC) were computed. The results show that various sensations could be successfully evoked for both amputees and non-disabled subjects by customizing stimulus parameters. The ERP confirmed the sensation and revealed the sensory-processing-related components like N100 and P200; the BEAMs confirmed the corresponding regions of somatosensory cortex were activated by stimulation; the FC indicated an increase of interactions between the regions of sensorimotor cortex. This study may shed light on how the brain responds to external stimulation as sensory feedback and serve as a pilot for further bidirectional closed-loop prosthetic control.

Influence of neck and back massage on the synchronicity of electrical brain activity

Due to busy working or learning schedules, people spend a lot of time in a sitting, motionless position, which could cause tension neck syndrome and promote other harmful consequences. Massage has been used for health support for many decades. However, there are a lot of different types of massages with different physiological effects, and our knowledge remains pure regarding the biological mechanisms of massage benefits. The question of the influence of massage on functional brain state attracts scientific interest as massage is an available and attractive health-supporting tool. Particular types of massage had been found to reduce frontal brain activity, but others were associated with the activation of certain brain regions. In our study, we aimed to evaluate the impact of combined general and point massage of the cervical-collar area on the electrical activity of the brain in terms of its synchronicity in healthy young subjects. Through cross-correlation analysis of EEG before and after massage, we found that massage of the cervical-collar area increased the synchronicity of electrical activity between the central and frontal regions during resting state with closed eyes and decreased the synchronicity between the central and occipital brain areas during resting state with opened eyes, which allows us to suggest that this type of massage optimizes functional brain state.

Music and Visual Imagery: Electroencephalographic Evidence of General and Modality-Specific Activity Patterns

There is a substantial corpus of evidence about the shared brain mechanism between imagery and perception. However, the psychophysiological exploration concerning possible mechanisms underlying modality-independent mental imagery and how much it involves or depends on other cognitive processes, such as attention and memory, is still scant. To address this, we performed an electroencephalographic (EEG) study to compare brain activity related to auditory/musical and visual imagery to identify shared and specific activity patterns. The electrical brain activity of 12 musicians was measured while they perceived or imagined either auditory or visual stimuli with two levels of complexity. Mean power differences were calculated between tasks and conditions for alpha, beta, and gamma bands. For the alpha band, an increase in mean relative power was observed in occipital and parietal areas throughout the paradigm. Regarding level of complexity of the imagery tasks, same-modality contrasts (i.e., auditory simple vs. auditory complex) showed small-magnitude differences in power that were only significant for a few locations along the midline. However, when comparing across imagery modalities (i.e., auditory simple vs. visual simple), the significantly different locations increased in number and were mainly distributed in central and occipital regions, where differences were positive for mean alpha power and negative for beta and gamma bands. With respect to resting (baseline) power, the main differences were observed as a wider distribution of beta power increments in relation to visual imagery tasks, and similarities were identified for both auditory and visual imagery tasks as an increase in gamma power in right frontal locations, increased frontal and pre-frontal beta power, and some bilateral parietal increased beta power. These results seem to support the hypothesis that mental imagery, to some extent, involves areas related to memory retrieval, attention, semantic processing, and motor preparation, in addition to the primary areas corresponding to each sensory modality.

Brian Electrical Activity Acquisition and Decoding Algorithms For Using BMIs

Physical disabilities in limbs often lead to severe impacts on personal life and work. However, BMI technology offers hope for amputees or paralyzed individuals to regain control and motor abilities. By reading signals from the brain's motor cortex, BMI technology enables the control of robotic arms, cursors, and more, facilitating functional recovery. The key lies in identifying and extracting information related to specific tasks or intentions from brain signals, where the decoder determines the accuracy, reliability, and sensitivity of the final movements. This paper aims to provide a brief overview of the decoding steps in BMIs. The paper will discuss how the raw signals are acquired from the brain, including invasive BMI and noninvasive BMI, along with the respective advantages and disadvantages of these two approaches. Then, it will explore how these signals are processed into specific commands through both linear and nonlinear algorithms. Additionally, the paper will discuss the strengths, limitations, and feasibility of different decoding methods in human-computer interaction applications. This review offers researchers a preliminary understanding of the acquisition and decoding algorithms for brain electrical activity in BMIs. By evaluating the strengths and limitations of different algorithms, we can provide guidance for designing and improving BMI systems, thereby supporting the achievement of more accurate and efficient human-computer interaction.

A Comprehensive electroencephalogram audit in a tertiary care center in Pakistan: demographic distribution, referral indications and findings

Background and Objective:&#x0D; Electroencephalography (EEG) is a widely employed non-invasive technique for recording brain electrical activity, pivotal for diagnosing various neurological conditions. The study aims to provide insight into demographic distribution, referral indications, and EEG findings in a tertiary care hospital in Pakistan.&#x0D; &#x0D; Methods:&#x0D; A retrospective cross-sectional analysis was conducted involving 2,924 patients who underwent routine EEG evaluations over a three-year period. Data was collected from the EEG records after approval from the ethical review committee. Demographic variables, referral reasons, and EEG findings were recorded and analyzed using descriptive statistics.&#x0D; &#x0D; Results:&#x0D; The study revealed a gender distribution of 41.35 % females and 58.65 % males, aligning with broader epidemiological trends. Participants were categorized into different age groups and most of the patients in our study belonged to the five to 18 years of age group. Most common indication was a suspected seizure disorder (78.32 %), followed by subclinical seizures (7.38 %) and medication adjustment (6.81 %). Notably, encephalopathy was the most common abnormal EEG finding (31 %), with temporal epilepsies being predominant within the focal seizure category.&#x0D; &#x0D; Conclusion:&#x0D; The descriptive nature of the study contributes novel insights. The study’s contribution provides valuable guidance for healthcare professionals, enhancing their comprehension of neurological conditions and refining diagnostic and treatment strategies.