Rhythmic Music Training's Effect on Syntactic Structure Processing among Malay Adolescents with Syntactic Specific Language Impairment.

Background: Adolescents with syntactic specific language impairment (S-SLI) fail to comprehend which object questions. We hypothesised that rhythmic music training is more effective in treating this condition than conventional methods because music is often perceived as having a clear, isochronous beat or pulse. Thus, this study aims to investigate the effects of rhythmic music training on the syntactic structure processing of Malay which questions among native adolescents. Methods: In this research study, the participants were three groups of Malay adolescents aged 13 years old-15 years old: i) adolescents with S-SLI with music training, ii) adolescents with S-SLI without music training and iii) typically developing adolescents. Before and after music training, the participants were given a sentence-picture matching task. Accuracy measures and reaction times were captured using E-Prime 2.0. Results: The results indicated that with music training, the accuracy and reaction time associated with which object questions among the two SLI groups were significantly higher and lower, respectively. Conclusion: The implications of using rhythmic music training in enhancing syntactic structure processing are also discussed.

A review of the holy Quran listening and its neural correlation for its potential as a psycho-spiritual therapy.

Since its revelation over 14 centuries ago, the Holy Quran is considered as scriptural divine words of Islam, and it is believed to promote psycho-spiritual therapeutic benefits to its reciter and/or listener. In this context, the listening of rhythmic Quranic verses among Muslims is often viewed as a form of unconventional melodic vocals, with accompanied anecdotal claims of the 'Quranic chills' pleasing effect. However, compared to music, rhythm, and meditation therapy, information on the neural basis of the anecdotal healing effects of the Quran remain largely unexplored. Current studies in this area took the leads from the low-frequency neuronal oscillations (i.e., alpha and theta) as the neural correlates, mainly using electroencephalography (EEG) and/or magnetoencephalography (MEG). In this narrative review, we present and discuss recent work related to these neural correlates and highlight several methodical issues and propose recommendations to progress this emerging transdisciplinary research. Collectively, evidence suggests that listening to rhythmic Quranic verses activates similar brain regions and elicits comparable therapeutic effects reported in music and rhythmic therapy. Notwithstanding, further research are warranted with more concise and standardized study designs to substantiate these findings, and opens avenue for the listening to Quranic verses as an effective complementary psycho-spiritual therapy.

Encephalitis in Patients with COVID-19: A Systematic Evidence-Based Analysis.

Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predominantly infects the respiratory system, several investigations have shown the involvement of the central nervous system (CNS) along the course of the illness, with encephalitis being one of the symptoms. The objective of this systematic review was to evaluate the characteristics (clinical, neuro-radiological aspects, and laboratory features) and outcomes of encephalitis in COVID-19 patients. PubMed, Scopus, and Google Scholar databases were searched from 1 December 2019 until 21 July 2022 to identify case reports and case series published on COVID-19 associated with encephalitis. The quality of the included studies was assessed by the Joanna Briggs Institute critical appraisal checklists. This systematic review included 79 studies, including 91 COVID-19 patients (52.7% male) experiencing encephalitis, where 85.6% were adults (49.3 ± 20.2 years), and 14.4% were children (11.2 ± 7.6 years). RT-PCR was used to confirm 92.2% of the COVID-19 patients. Encephalitis-related symptoms were present in 78.0% of COVID-19 patients at the time of diagnosis. In these encephalitis patients, seizure (29.5%), confusion (23.2%), headache (20.5%), disorientation (15.2%), and altered mental status (11.6%) were the most frequently reported neurologic manifestations. Looking at the MRI, EEG, and CSF findings, 77.6%, 75.5%, and 64.1% of the patients represented abnormal results. SARS-CoV-2-associated or -mediated encephalitis were the most common type observed (59.3%), followed by autoimmune encephalitis (18.7%). Among the included patients, 66.7% were discharged (37.8% improved and 28.9% fully recovered), whereas 20.0% of the reported COVID-19-positive encephalitis patients died. Based on the quality assessment, 87.4% of the studies were of high quality. Although in COVID-19, encephalitis is not a typical phenomenon, SARS-CoV-2 seems like a neuropathogen affecting the brain even when there are no signs of respiratory illness, causing a high rate of disability and fatality.

A Cumulants-Based Human Brain Decoding.

Human cognition is influenced by the way the nervous system processes information and is linked to this mechanical explanation of the human body's cognitive function. Accuracy is the key emphasis in neuroscience which may be enhanced by utilising new hardware, mathematical, statistical, and computational methodologies. Feature extraction and feature selection also play a crucial function in gaining improved accuracy since the proper characteristics can identify brain states efficiently. However, both feature extraction and selection procedures are dependent on mathematical and statistical techniques which implies that mathematical and statistical techniques have a direct or indirect influence on prediction accuracy. The forthcoming challenges of the brain-computer interface necessitate a thorough critical understanding of the complicated structure and uncertain behavior of the brain. It is impossible to upgrade hardware periodically, and thus, an option is necessary to collect maximum information from the brain against varied actions. The mathematical and statistical combination could be the ideal answer for neuroscientists which can be utilised for feature extraction, feature selection, and classification. That is why in this research a statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy. A score fusion function is changed utilising an enhanced cumulants-driven likelihood ratio test employing multivariate pattern analysis. Functional MRI data were acquired from 12 patients versus a visual test that comprises of pictures from five distinct categories. After cleaning the data, feature extraction and selection were done using mathematical approaches, and lastly, the best match of the projected class was established using the likelihood ratio test. To validate the suggested approach, it is compared with the current methods reported in recent research.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

sLORETA Source Localisation of Visual Mismatch Negativity in Dyslexic Children During Malay Orthographical Lexicon Stimulations.

BACKGROUND: While there are studies on visual lexical processing in other languages among dyslexics, no studies were done in the Malay language. The origin of visual lexical processing might be different in the Malay language. We aimed to detect the source localisation of visual mismatch negativity (vMMN) during Malay orthographic lexicon stimulations, employing an event-related potential (ERP) study. METHODS: Twelve dyslexic and twelve non-dyslexic children participated in this study. They pushed button '1' when they saw real (meaningful) Malay words and button '2' for pseudowords (meaningless). The source localisation of vMMN was performed in the grand average waveform by applying the standardised low-resolution brain electromagnetic tomography (sLORETA) method using Net Station software. RESULTS: Left occipital (BA17) and left temporal (BA37) lobes were activated during real words in the non-dyslexic and dyslexic children, respectively. During pseudowords, BA18 and BA17 areas of the left occipital lobe were activated in the non-dyslexic and dyslexic children, separately. vMMN sources were found at the left temporal (BA37) and right frontal (BA11) lobes in non-dyslexic and dyslexic children, respectively. CONCLUSION: Right frontal lobe is the decision-making area where vMMN source was found in dyslexic children. We concluded that dyslexic children required the decision-making area to detect Malay real and pseudowords.

Acute application of Centella asiatica extract enhanced AMPAR-mediated postsynaptic currents in rat entorhinal cortex.

Centella asiatica is notable for its wide range of biological activities beneficial to human health, particularly its cognitive enhancement and neuroprotective effects. The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors are ionotropic glutamate receptors mediating fast excitatory neurotransmission essential in long-term potentiation widely thought to be the cellular mechanism of learning and memory. The method of whole-cell patch-clamp was used to study the effect of the acute application of Centella asiatica extract on the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor-mediated spontaneous excitatory postsynaptic currents in the entorhinal cortex of rat brain slices. The respective low dose of test compounds significantly increased the amplitude of spontaneous excitatory postsynaptic currents while having no significant effects on the frequency. The findings suggested that Centella asiatica extract increased the response of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors at the postsynaptic level, revealing the potential role of Centella asiatica in modulating the glutamatergic responses in the entorhinal cortex of rat brain slices to produce cognitive enhancement effects.

Differential expression of entorhinal cortex and hippocampal subfields α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors enhanced learning and memory of rats following administration of Centella asiatica.

Centella asiatica (CA) is a widely used traditional herb, notably for its cognitive enhancing effect and potential to increase synaptogenesis. The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-methyl-D-aspartate receptors (NMDARs) mediate fast excitatory neurotransmission with key roles in long-term potentiation which is believed to be the cellular mechanism of learning and memory. Improved learning and memory can be an indication to the surface expression level of these receptors. Our previous study demonstrated that administration of CA extract improved learning and memory and enhanced expression of AMPAR GluA1 subunit while exerting no significant effects on GABAA receptors of the hippocampus in rats. Hence, to further elucidate the effects of CA, this study investigated the effects of CA extract in recognition memory and spatial memory, and its effects on AMPAR GluA1 and GluA2 subunit and NMDAR GluN2 A and GluN2B subunit expression in the entorhinal cortex (EC) and hippocampal subfields CA1 and CA3. The animals were administered with saline, 100 mg/kg, 300 mg/kg, and 600 mg/kg of CA extract through oral gavage for 14 days, followed by behavioural analysis through Open Field Test (OFT), Novel Object Recognition Task (NORT), and Morris Water Maze (MWM) and lastly morphological and immunohistochemical analysis of the surface expression of AMPAR and NMDAR subunits were performed. The results showed that 14 days of administration of 600 mg/kg of CA extract significantly improved memory assessed through NORT while 300 mg/kg of CA extract significantly improved memory of the animals assessed through MWM. Immunohistochemical analysis revealed differential modulation effects on the expressions of receptor subunits across CA1, CA3 and EC. The CA extract at the highest dose (600 mg/kg) significantly enhanced the expression of AMPAR subunit GluA1 and GluA2 in CA1, CA3 and EC, and NMDAR subunit GluN2B in CA1 and CA3 compared to control. At 300 mg/kg, CA significantly increased expression of AMPAR GluA1 in CA1 and EC, and GluA2 in CA1, CA3 and EC while 100 mg/kg of CA significantly increased expression of only AMPAR subunit GluA2 in CA3 and EC. Expression of NMDAR subunit GluN2 A was significantly reduced in the CA3 (at 100, 300, and 600 mg/kg) while no significant changes of subunit expression was observed in CA1 and EC compared to control. The results suggest that the enhanced learning and memory observed in animals administered with CA was mainly mediated through increased expression of AMPAR GluA1 and GluA2 subunits and differential expression of NMDAR GluN2 A and GluN2B subunits in the hippocampal subfields and EC. With these findings, the study revealed a new aspect of cognitive enhancing effect of CA and its therapeutic potentials through modulating receptor subunit expression.

A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA.

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

Language Mapping in Awake Surgery: Report of Two Cases with Review of Language Networks.

The role of language in communication plays a crucial role in human development and function. In patients who have a surgical lesion at the functional language areas, surgery should be intricately planned to avoid incurring further morbidity. This normally requires extensive functional and anatomical mappings of the brain to identify regions that are involved in language processing and production. In our case report, regions of the brain that are important for language functions were studied before surgery by employing (a) extraoperative methods such as functional magnetic resonance imaging, transmagnetic stimulation, and magnetoencephalography; (b) during the surgery by utilizing intraoperative awake surgical methods such as an intraoperative electrical stimulation; and (c) a two-stage surgery, in which electrical stimulation and first mapping are made thoroughly in the ward before second remapping during surgery. The extraoperative methods before surgery can guide the neurosurgeon to localize the functional language regions and tracts preoperatively. This will be confirmed using single-stage intraoperative electrical brain stimulation during surgery or a two-stage electrical brain stimulation before and during surgery. Here, we describe two cases in whom one has a superficial lesion and another a deep-seated lesion at language-related regions, in which language mapping was done to preserve its function. Additional review on the neuroanatomy of language regions, language network, and its impairment was also described.

Role of Pre-Synaptic NMDA Receptors in the Modulation of Inhibitory Synaptic Transmission in Sensory-Motor and Visual Cortical Pyramidal Neurons in Brain Slices of Young Epileptic Mice.

BACKGROUND: Previous studies from animal models have shown that pre-synaptic NMDA receptors (preNMDARs) are present in the cortex, but the role of inhibition mediated by preNMDARs during epileptogenesis remains unclear. In this study, we wanted to observe the changes in GABAergic inhibition through preNMDARs in sensory-motor and visual cortical pyramidal neurons after pilocarpine-induced status epilepticus. METHODS: Using a pilocarpine-induced epileptic mouse model, sensory-motor and visual cortical slices were prepared, and the whole-cell patch clamp technique was used to record spontaneous inhibitory post-synaptic currents (sIPSCs). RESULTS: The primary finding was that the mean amplitude of sIPSC from the sensory-motor cortex increased significantly in epileptic mice when the recording pipette contained MK-801 compared to control mice, whereas the mean sIPSC frequency was not significantly different, indicating that post-synaptic mechanisms are involved. However, there was no significant pre-synaptic inhibition through preNMDARs in the acute brain slices from pilocarpine-induced epileptic mice. CONCLUSION: In the acute case of epilepsy, a compensatory mechanism of post-synaptic inhibition, possibly from ambient GABA, was observed through changes in the amplitude without significant changes in the frequency of sIPSC compared to control mice. The role of preNMDAR-mediated inhibition in epileptogenesis during the chronic condition or in the juvenile stage warrants further investigation.

Hormonal Influences on Cognitive Function.

This article examines how hormonal changes may affect the neuronal networking and mechanisms of cognitive function. Hormones are the chemical regulators of the human body and function critically to maintain various processes, such as growth, emotions and even cognition. Numerous studies have examined the relationship between hormonal effects and cognitive function; these studies have investigated different factors, such as aging, pregnancy, post-natal states, emotions and stress. Different types of hormones produce different outcomes for the human body and mind. Hormones may also contribute to both positive and negative outcomes, depending on whether the hormone levels are too low or too high. To investigate the hormonal effects on cognitive function, the sources of localisation must be localised, so that the neuronal network can be realised. Furthermore, cognitive function does not rely on a specific brain region but is determined by the neuronal network interactions. Thus, it is worthwhile to know the neural mechanisms behind cognitive functions that are affected by hormones.

Trkb-IP3 Pathway Mediating Neuroprotection in Rat Hippocampal Neuronal Cell Culture Following Induction of Kainic Acid.

BACKGROUND: Following brain injury, development of hippocampal sclerosis often led to the temporal lobe epilepsy which is sometimes resistant to common anti-epileptic drugs. Cellular and molecular changes underlying epileptogenesis in animal models were studied, however, the underlying mechanisms of kainic acid (KA) mediated neuronal damage in rat hippocampal neuron cell culture alone has not been elucidated yet. METHODS: Embryonic day 18 (E-18) rat hippocampus neurons were cultured with poly-L-lysine coated glass coverslips. Following optimisation, KA (0.5 µM), a chemoconvulsant agent, was administered at three different time-points (30, 60 and 90 min) to induce seizure in rat hippocampal neuronal cell culture. We examined cell viability, neurite outgrowth density and immunoreactivity of the hippocampus neuron culture by measuring brain derived neurotrophic factor (BDNF), γ-amino butyric acid A (GABAA) subunit α-1 (GABRA1), tyrosine receptor kinase B (TrkB), and inositol trisphosphate receptor (IP3R/IP3) levels. RESULTS: The results revealed significantly decreased and increased immunoreactivity changes in TrkB (a BDNF receptor) and IP3R, respectively, at 60 min time point. CONCLUSION: The current findings suggest that TrkB and IP3 could have a neuroprotective role which could be a potential pharmacological target for anti-epilepsy drugs.

Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates.

Visual cognitive function is important to build up executive function in daily life. Perception of visual Number form (e.g., Arabic digit) and numerosity (magnitude of the Number) is of interest to cognitive neuroscientists. Neural correlates and the functional measurement of Number representations are complex occurrences when their semantic categories are assimilated with other concepts of shape and colour. Colour perception can be processed further to modulate visual cognition. The Ishihara pseudoisochromatic plates are one of the best and most common screening tools for basic red-green colour vision testing. However, there is a lack of study of visual cognitive function assessment using these pseudoisochromatic plates. We recruited 25 healthy normal trichromat volunteers and extended these studies using a 128-sensor net to record event-related EEG. Subjects were asked to respond by pressing Numbered buttons when they saw the Number and Non-number plates of the Ishihara colour vision test. Amplitudes and latencies of N100 and P300 event related potential (ERP) components were analysed from 19 electrode sites in the international 10-20 system. A brain topographic map, cortical activation patterns and Granger causation (effective connectivity) were analysed from 128 electrode sites. No major significant differences between N100 ERP components in either stimulus indicate early selective attention processing was similar for Number and Non-number plate stimuli, but Non-number plate stimuli evoked significantly higher amplitudes, longer latencies of the P300 ERP component with a slower reaction time compared to Number plate stimuli imply the allocation of attentional load was more in Non-number plate processing. A different pattern of asymmetric scalp voltage map was noticed for P300 components with a higher intensity in the left hemisphere for Number plate tasks and higher intensity in the right hemisphere for Non-number plate tasks. Asymmetric cortical activation and connectivity patterns revealed that Number recognition occurred in the occipital and left frontal areas where as the consequence was limited to the occipital area during the Non-number plate processing. Finally, the results displayed that the visual recognition of Numbers dissociates from the recognition of Non-numbers at the level of defined neural networks. Number recognition was not only a process of visual perception and attention, but it was also related to a higher level of cognitive function, that of language.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Early visual analysis tool using magnetoencephalography for treatment and recovery of neuronal dysfunction.

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Simultaneous EEG-fMRI for working memory of the human brain.

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Decoding of visual information from human brain activity: A review of fMRI and EEG studies.

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

Object categories specific brain activity classification with simultaneous EEG-fMRI.

Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature.