Comparison of resting electroencephalogram coherence in patients with mild cognitive impairment and normal elderly subjects

Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population.

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

Electroencephalogram utilization and psychiatric comorbidities among children and adolescents with epilepsy in rural Southwestern Uganda

Objective This study aimed at describing routine electroencephalogram (EEG) findings among children and adolescents with a clinical diagnosis of epilepsy and determines how interictal EEG abnormalities vary with the psychiatric comorbidities. Methods We conducted a cross-sectional study among children and adolescents with epilepsy aged 5–18 years receiving care from a regional referral hospital in Southwestern Uganda. Psychiatric comorbidities were assessed using an adapted parent version of Child and Adolescent Symptom Inventory-5. Thirty-minute EEG samples were taken from routine EEG recordings that were locally performed and remotely interpreted for all participants. Results Of the 140 participants, 71 (50.7%) had normal EEG findings and 51 (36.4%) had epileptiform abnormalities while 18 (12.9%) had non-epileptiform. Of those who had epileptiform abnormalities on EEG, 23 (45.1%) were focal, 26 (51.0%) were generalized, and 2 (3.9%) were focal with bilateral spread. There was no significant association between the different psychiatric comorbidities and the interictal EEG abnormalities. Conclusions Among children and adolescents with a clinical diagnosis of epilepsy in Southwestern Uganda, only 36% showed epileptiform abnormalities on their EEG recordings. There was no association between the interictal EEG abnormalities and psychiatric comorbidities.

Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

Electrophysiological Correlates of Shyness Affected by Facial Attractiveness

Previous neurological studies of shyness have focused on the hemispheric asymmetry of alpha spectral power. To the best of our knowledge, few studies have focused on the interaction between different frequencies bands in the brain of shyness. Additionally, shy individuals are even shyer when confronted with a group of people they consider superior to them. This study aimed to reveal the neural basis of shy individuals using the delta-beta correlation. Further, it aimed to investigate the effect of evaluators’ facial attractiveness on the delta-beta correlation of shyness during the speech anticipation phase. We recorded electroencephalogram (EEG) activity of 94 participants during rest and anticipation of the public speaking phase. Moreover, during the speech anticipation phase, participants were presented with high or low facial attractiveness. The results showed that, as predicted, the delta-beta correlation in the frontal region was more robust for high shyness than for low shyness during the speech anticipation phase. However, no significant differences were observed in the delta-beta correlation during the baseline phase. Further exploration found that the delta-beta correlation was more robust for high facial attractiveness than low facial attractiveness in the high shyness group. However, no significant difference was found in the low-shyness group. This study suggests that a stronger delta-beta correlation might be the neural basis for shy individuals. Moreover, high facial attractiveness might enhance the delta-beta correlation of high shyness in anticipation of public speaking.

Predictive Processing in Poetic Language: Event-Related Potentials Data on Rhythmic Omissions in Metered Speech

Predictions during language comprehension are currently discussed from many points of view. One area where predictive processing may play a particular role concerns poetic language that is regularized by meter and rhyme, thus allowing strong predictions regarding the timing and stress of individual syllables. While there is growing evidence that these prosodic regularities influence language processing, less is known about the potential influence of prosodic preferences (binary, strong-weak patterns) on neurophysiological processes. To this end, the present electroencephalogram (EEG) study examined whether the predictability of strong and weak syllables within metered speech would differ as a function of meter (trochee vs. iamb). Strong, i.e., accented positions within a foot should be more predictable than weak, i.e., unaccented positions. Our focus was on disyllabic pseudowords that solely differed between trochaic and iambic structure, with trochees providing the preferred foot in German. Methodologically, we focused on the omission Mismatch Negativity (oMMN) that is elicited when an anticipated auditory stimulus is omitted. The resulting electrophysiological brain response is particularly interesting because its elicitation does not depend on a physical stimulus. Omissions in deviant position of a passive oddball paradigm occurred at either first- or second-syllable position of the aforementioned pseudowords, resulting in a 2-by-2 design with the factors foot type and omission position. Analyses focused on the mean oMMN amplitude and latency differences across the four conditions. The result pattern was characterized by an interaction of the effects of foot type and omission position for both amplitudes and latencies. In first position, omissions resulted in larger and earlier oMMNs for trochees than for iambs. In second position, omissions resulted in larger oMMNs for iambs than for trochees, but the oMMN latency did not differ. The results suggest that omissions, particularly in initial position, are modulated by a trochaic preference in German. The preferred strong-weak pattern may have strengthened the prosodic prediction, especially for matching, trochaic stimuli, such that the violation of this prediction led to an earlier and stronger prediction error. Altogether, predictive processing seems to play a particular role in metered speech, especially if the meter is based on the preferred foot type.

A case of Landau-Kleffner syndrome with SLC26A4-related hearing impairment

Background Landau-Kleffner syndrome (LKS) is an acquired aphasia and electroencephalogram (EEG) abnormalities mainly in temporoparietal areas. SLC26A4 mutations can cause hearing loss associated with enlarged vestibular aqueduct (EVA). Case presentations We report a case of LKS in a 5-year-old boy with non-syndromic EVA due to homozygous mutations of c.919-2A>G (IVS7-2A>G) in SLC26A4. He had normal language development before 2 years old. At the age of 2.5 years, he was admitted to the hospital due to remarkable language delay, and diagnosed with hearing loss with EVA. The seizures started at 4.4 years of age and EEG recording showed electrical status epilepticus during sleep (ESES) with a posterior-temporal predominance. He received cochlear implantation in the right ear at 4.7 years of age, which improved his hearing and language skills. The nocturnal focal motor seizures recurred at 4.9 years of age. Then a remarkable inability to respond to calls and reduction in spontaneous speech were noticed. He was treated with methylprednisolone at 5 years old, which controlled the seizures, suppressed ESES, and remarkably improved the language ability. The absence of seizures maintained until the last follow-up at 5.3 years of age, with further improvements in EEG recording and language ability. Conclusions The co-existence of LKS and hearing loss caused by SLC26A4 mutations increases the difficulty of LKS diagnosis, especially in the presence of hearing loss and impaired language skills. EEG discharges predominantly in temporoparietal areas, the occurrence of ESES, and language improvement after antiepileptic medications are potential indicators for LKS diagnosis.

Novel Methods for Elucidating Modality Importance in Multimodal Electrophysiology Classifiers

Multimodal classification is increasingly common in biomedical informatics studies. Many such studies use deep learning classifiers with raw data, which makes explainability difficult. As such, only a few studies have applied explainability methods, and new methods are needed. In this study, we propose sleep stage classification as a testbed for method development and train a convolutional neural network with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global approach that is uniquely adapted for electrophysiology analysis. We further present two local approaches that can identify subject-level differences in explanations that would be obscured by global methods and that can provide insight into the effects of clinical and demographic variables upon the patterns learned by the classifier. We find that EEG is globally the most important modality for all sleep stages, except non-rapid eye movement stage 1 and that local subject-level differences in importance arise. We further show that sex, followed by medication and age had significant effects upon the patterns learned by the classifier. Our novel methods enhance explainability for the growing field of multimodal classification, provide avenues for the advancement of personalized medicine, and yield novel insights into the effects of demographic and clinical variables upon classifiers.