Perception of surface stickiness in different sensory modalities: an functional MRI study

Surface texture can be perceived not only from tactile, but also from auditory and visual sensory cues. In our previous psychophysical study, we demonstrated that humans can recognize surface stickiness using only one kind of sensory modality without any difficulty. However, the brain regions that would be activated by non-corresponding sensory cues, for example, auditory and visual cues, remain unknown. In this human functional MRI study, we explored brain regions associated with surface stickiness perception in each of three different sensory modalities, and sought for common neural activities across modalities. In the tactile condition, participants actually touched a sticky surface with their right index finger. In the auditory and visual conditions, audio and video clips of tactile explorations of a sticky surface were presented and participants were asked to recall the perceived stickiness as vividly as possible. Our results, based on a general linear model analysis, showed that somatosensory cortices including postcentral gyrus, anterior insula, and anterior intraparietal sulcus were significantly activated across all modalities. Moreover, we observed significant activation of primary sensory regions of each modality. A follow-up conjunction analysis identified that postcentral gyrus, anterior intraparietal sulcus, precentral gyrus, and supplementary motor area were activated in common. These findings could deepen our understanding of the surface stickiness perception in the human brain

Online home appliance control using EEG-Based brain-computer interfaces

Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.open

Neuronal ensemble decoding using a dynamical maximum entropy model

As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.open0

Gamma EEG Correlates of Haptic Preferences for a Dial Interface

Consumers often develop preferences toward consumer electronics based not only on the visual appearance of a product, but also on its haptic interface. If consumers express a strong haptic preference for a consumer electronic product, they are more likely to purchase it. Hence, it is important to understand how consumers' haptic preference for consumer electronics is formed. Conventional paper-based methods may not provide sufficient information for this purpose, because they provide post-event (i.e., after haptic experience) and environment-dependent (i.e., depending on the manner of asking questions, the person asking the questions, and so on.) data. Therefore, the present study investigated haptic preferences for consumer electronics using neural responses during haptic experiences, which provide the advantage of observing changes while the user is manipulating the product and obtaining environment-independent data. We measured neural responses using non-invasive electroencephalography (EEG). Eighteen volunteers participated in the study and manipulated a haptic dial knob that generates four different haptic profiles; during the manipulation, their EEG signals were recorded. After experiencing different haptic profiles, participants reported their level of preference for each profile. The analysis of EEG revealed that frontal gamma oscillations correlate with the level of haptic preferences, with oscillations becoming stronger with increasing haptic preference. The highest correlation between frontal gamma power and haptic preference was found in the early period of the dial task. Therefore, the frontal gamma oscillation of the EEG may represent a neural correlate of the haptic preference and provides a neural basis for understanding this preference in relation to consumer electronics

Modeling and Analysis of Neural Spike Trains

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Simultaneous deletion of floxed genes mediated by CaMKIIa-Cre in the brain and in male germ cells: application to conditional and conventional disruption of Go-alfa

The Cre/LoxP system is a well-established approach to spatially and temporally control genetic inactivation. The calcium/calmodulin-dependent protein kinase II alpha subunit (CaMKIIα) promoter limits expression to specific regions of the forebrain and thus has been utilized for the brain-specific inactivation of the genes. Here, we show that CaMKIIα-Cre can be utilized for simultaneous inactivation of genes in the adult brain and in male germ cells. Double transgenic Rosa26+/stop-lacZ::CaMKIIα-Cre+/Cre mice generated by crossing CaMKIIα-Cre+/Cre mice with floxed ROSA26 lacZ reporter (Rosa26+/stop-lacZ) mice exhibited lacZ expression in the brain and testis. When these mice were mated to wild-type females, about 27% of the offspring were whole body blue by X-gal staining without inheriting the Cre transgene. These results indicate that recombination can occur in the germ cells of male Rosa26+/stop-lacZ::CaMKIIα-Cre+/Cre mice. Similarly, when double transgenic Gnao+/f::CaMKIIα-Cre+/Cre mice carrying a floxed Go-alpha gene (Gnaof/f) were backcrossed to wild-type females, approximately 22% of the offspring carried the disrupted allele (GnaoΔ) without inheriting the Cre transgene. The GnaoΔ/Δ mice closely resembled conventional Go-alpha knockout mice (Gnao−/−) with respect to impairment of their behavior. Thus, we conclude that CaMKIIα-Cre mice afford recombination for both tissue- and time-controlled inactivation of floxed target genes in the brain and for their permanent disruption. This work also emphasizes that extra caution should be exercised in utilizing CaMKIIα-Cre mice as breeding pairs.Fil: Choi, Chan-Il. Ajou University. School of Medicine; Corea del SurFil: Yoon, Sang-Phil. Ajou University. School of Medicine; Corea del SurFil: Choi, Jung-Mi. Ajou University. School of Medicine; Corea del SurFil: Kim, Sung-Soo. Ajou University. School of Medicine; Corea del SurFil: Lee, Young-Don. Ajou University. School of Medicine; Corea del SurFil: Birnbaumer, Lutz. National Institute of Environmental Health Sciences; Estados Unidos. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; ArgentinaFil: Suh-Kim. Haeyoung. Ajou University. School of Medicine; Corea del Su

Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures

Improvement of P300-Based Brain-Computer Interfaces for Home Appliances Control by Data Balancing Techniques

The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek's links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem

Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study

Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (-2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13-30Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.open