Effects of bihemispheric transcranial direct current stimulation on motor recovery in subacute stroke patients: a double-blind, randomized sham-controlled trial.

BACKGROUND: Bihemispheric transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) can simultaneously modulate bilateral corticospinal excitability and interhemispheric interaction. However, how tDCS affects subacute stroke recovery remains unclear. We investigated the effects of bihemispheric tDCS on motor recovery in subacute stroke patients. METHODS: We enrolled subacute inpatients who had first-ever ischemic stroke at subcortical regions and moderate-to-severe baseline Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score 2-56. Participants between 14 and 28 days after stroke were double-blind, randomly assigned (1:1) to receive real (n = 13) or sham (n = 14) bihemispheric tDCS (with ipsilesional M1 anode and contralesional M1 cathode, 20 min, 2 mA) during task practice twice daily for 20 sessions in two weeks. Residual integrity of the ipsilesional corticospinal tract was stratified between groups. The primary efficacy outcome was the change in FMA-UE score from baseline (responder as an increase ≥ 10). The secondary measures included changes in the Action Research Arm Test (ARAT), FMA-Lower Extremity (FMA-LE) and explorative resting-state MRI functional connectivity (FC) of target regions after intervention and three months post-stroke. RESULTS: Twenty-seven participants completed the study without significant adverse effects. Nineteen patients (70%) had no recordable baseline motor-evoked potentials (MEP-negative) from the paretic forearm. Compared with the sham group, the real tDCS group showed enhanced improvement of FMA-UE after intervention (p < 0.01, effect size η2 = 0.211; responder rate: 77% vs. 36%, p = 0.031), which sustained three months post-stroke (p < 0.01), but not ARAT. Interestingly, in the MEP-negative subgroup analysis, the FMA-UE improvement remained but delayed. Additionally, the FMA-LE improvement after real tDCS was not significantly greater until three months post-stroke (p < 0.01). We found that the individual FMA-UE improvements after real tDCS were associated with bilateral intrahemispheric, rather than interhemispheric, FC strengths in the targeted cortices, while the improvements after sham tDCS were associated with predominantly ipsilesional FC changes after adjustment for age and sex (p < 0.01). CONCLUSIONS: Bihemispheric tDCS during task-oriented training may facilitate motor recovery in subacute stroke patients, even with compromised corticospinal tract integrity. Further studies are warranted for tDCS efficacy and network-specific neuromodulation. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov: (ID: NCT02731508).

Characterization of the role of endogenous nitric oxide in myogenic vascular oscillations during cooling-evoked hemodynamic perturbations of rats.

Rapid immersion of a rat's limbs into 4 °C water, a model of cold stress, can elicit hemodynamic perturbations (CEHP). We previously reported that CEHP is highly relevant to sympathetic activation and nitric oxide production. This study identifies the role of nitric oxide in CEHP. Conscious rats were pretreated with the nitric oxide synthase inhibitor L-NAME (NG-nitro-l-arginine methyl ester) alone or following the removal of sympathetic influences using hexamethonium or guanethidine. Rats were then subjected to a 10 min cold-stress trial. Hemodynamic indices were telemetrically monitored throughout the experiment. The analyses included measurements of systolic blood pressure; heart rate; dicrotic notch; short-term cardiovascular oscillations and coherence between blood pressure variability and heart rate variability in regions of very low frequency (0.02-0.2 Hz), low frequency (0.2-0.6 Hz), and high frequency (0.6-3.0 Hz). We observed different profiles of hemodynamic reaction between hexamethonium and guanethidine superimposed on L-NAME, suggesting an essential role for a functional adrenal medulla release of epinephrine under cold stress. These results indicate that endogenous nitric oxide plays an important role in the inhibition of sympathetic activation and cardiovascular oscillations in CEHP.

Extractions of steady-state auditory evoked fields in normal subjects and tinnitus patients using complementary ensemble empirical mode decomposition.

BACKGROUND: Auditory steady-state response (ASSR) induced by repetitive auditory stimulus is commonly used for audiometric testing. ASSR can be measured using electro-encephalography (EEG) and magnetoencephalography (MEG), referred to as steady-state auditory evoked potential (SSAEP) and steady-state auditory evoked field (SSAEF), respectively. However, the signal level of SSAEP and SSAEF are weak so that signal processing technique is required to increase its signal-to-noise ratio. In this study, a complementary ensemble empirical mode decomposition (CEEMD)-based approach is proposed in MEG study and the extraction of SSAEF has been demonstrated in normal subjects and tinnitus patients. METHODS: The CEEMD utilizes noise assisted data analysis (NADA) approach by adding positive and negative noise to decompose MEG signals into complementary intrinsic mode functions (IMF). Ten subjects (five normal and five tinnitus patients) were studied. The auditory stimulus was designed as 1 kHz carrier frequency with 37 Hz modulation frequency. Two channels in the vicinities of right and left temporal areas were chosen as channel-of-interests (COI) and decomposed into IMFs. The spatial distribution of each IMF was correlated with a pair of left- and right-hemisphere spatial templates, designed from each subject's N100m responses in pure-tone auditory stimulation. IMFs with spatial distributions highly correlated with spatial templates were identified using K-means and those SSAEF-related IMFs were used to reconstruct noise-suppressed SSAEFs. RESULTS: The current strengths estimated from CEEMD processed SSAEF showed neural activities greater or comparable to those processed by conventional filtering method. Both the normal and tinnitus groups showed the phenomenon of right-hemisphere dominance. The mean current strengths of auditory-induced neural activities in tinnitus group were larger than the normal group. CONCLUSIONS: The present study proposes an effective method for SSAEF extraction. The enhanced SSAEF in tinnitus group echoes the decreased inhibition in tinnitus's central auditory structures as reported in previous studies.

Role of Efferent Sympathoadrenal Effects in Cooling-Induced Hemodynamic Perturbations in Rats: An Investigation by Spectrum Analysis.

Cold stress may produce hemodynamic perturbations but the underlying mechanisms are still not clear. Spectral analysis was used in this study to explore that sympathoadrenal activation could be involved in mechanisms of hemodynamic perturbations to cooling. Conscious rats after treatment with a control vehicle (saline) compared with withdrawal of sympathetic influences by ganglion blocker hexamethonium (HEX) or chemical sympathectomy guanethidine (GUA) were challenged by stressful cooling as acute immersing all four extremities in ice water (4 ± 2°C) for 10 min. Plasma nitric oxide (NO) and the appearance of Dichroitic notch (DN) were measured in comparison between treatment groups throughout the experimental course. Hemodynamic indices were telemetrically monitored, and variability of blood pressure and heart rate (BPV; HRV) were assessed over a range of frequencies: very-low frequency (VLF: 0.02-0.2 Hz), low frequency (LF: 0.2-0.6 Hz), high frequency (HF: 0.6-3 Hz), normalized (n)LF, nHF, ratio LF/HF of HRV (LF/HF(HRV)), and total power (TP: ≤3 Hz). Results showed that the concomitant reciprocal changes of spectral powers existed between frequencies of BPV and HRV to the stressful cooling (i.e. VLF(BPV) versus VLF(HRV), LF(BPV) versus LF(HRV), and nLF(BPV) versus nLF(HRV)) which contribute to the underlying mechanisms of sympathetic efferent influences and myogenic cardiovascular responsiveness. Furthermore, compared with the control vehicle in the stressful cooling, HEX restrained the increase of the pressor, tachycardia and VLF(BPV), except that VLF(HRV) was reduced. GUA abolished pressor, however, restrained the increase of the tachycardia, VLF(BPV) and LF(BPV). In addition, GUA reversed the downward tendency of nLF(BPV) into an upward tendency and attenuated both nLF(HRV) and LF/HF(HRV). DN was virtually undetectable after HEX management but was apparently noticeable after GUA management. Finally, the increase of plasma NO after cooling was diminished after HEX or GUA management. Taken together, these results substantiate that the spectral changes during stressful cooling are highly relevant to the efferent sympathetic rhythmicity and subsequent NO production.

Neuromagnetic index of hemispheric asymmetry predicting long-term outcome in sudden hearing loss.

The neuromagnetic index of hemispheric asymmetry in terms of ipsilateral/contralateral ratio at acute stage was previously revealed to prognosticate the 1-month hearing outcome of acute unilateral idiopathic sudden sensorineural hearing loss (ISSNHL), showing a dynamic relationship between top- and down-levels of auditory pathway. However, the prognostic effect of reorganization pattern for the long-term results remained elusive. This study aimed to probe the prognosticating relevance of hemispheric asymmetry to the hearing at chronic stage of ISSNHL. Using magnetoencephalography (MEG), inter-hemispheric differences in peak dipole of N100m responses to monaural tones were evaluated in 21 controls and 21 ISSNHL patients at initial and final (12 months later) stages. Predictive value of hemispheric asymmetry was assessed by correlating hearing level and ipsilateral/contralateral ratio (I/C) of N100m latency and amplitude. Healthy-side dominance of N100m was observed in ISSNHL initially, and remained in three final prognostic subgroups (complete, partial, and no recovery) of ISSNHL. The initial I/C(amplitude) on affected-ear stimulation strongly correlated with the hearing level of final stage in ISSNHL. However, there was no prognostic effect of hemispheric asymmetry pattern for the 12-month hearing improvement. The heterogeneity between neuromagnetic index and hearing levels possibly echoed different pathogeneses of ISSNHL. Since a restored hearing status did not necessarily lead toward a normal functional organization, the dynamics of hemispheric asymmetry could actually index a central resilient reorganization in the brain for sound processing in ISSNHL. Our finding showed not only a clinically relevant measure to predict final hearing of ISSNHL, but also a linkage between central plasticity and cochlear lesion. This finding suggests a new perspective, and perhaps new interventions, to diagnose and treat unilateral ISSNHL.

Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine.

BACKGROUND: Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject's physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual's difference in SSVEP is needed but was seldom reported. METHODS: This paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user's gaze targets. RESULTS: The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76%), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the information transfer rate (ITR) was 50.91 ± 8.70 bits/min. CONCLUSIONS: The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject's SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.

Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.

The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.

Association of polymorphisms in BDNF, MTHFR, and genes involved in the dopaminergic pathway with memory in a healthy Chinese population.

The contribution of genetic factors to the memory is widely acknowledged. Research suggests that these factors include genes involved in the dopaminergic pathway, as well as the genes for brain-derived neurotrophic factor (BDNF) and methylenetetrahydrofolate reductase (MTHFR). The activity of the products of these genes is affected by single nucleotide polymorphisms (SNPs) within the genes. This study investigates the association between memory and SNPs in genes involved in the dopaminergic pathway, as well as in the BDNF and MTHFR genes, in a sample of healthy individuals. The sample includes 134 Taiwanese undergraduate volunteers of similar cognitive ability. The Chinese versions of the Wechsler Memory Scale (WMS-III) and Wechsler Adult Intelligence Scale (WAIS-III) were employed. Our findings indicate that the BDNF Met66Val polymorphism and dopamine receptor D3 (DRD3) Ser9Gly polymorphism are associated significantly with long-term auditory memory. Further analysis detects no significant associations in the other polymorphisms and indices. Future replicated studies with larger sample sizes, and studies that consider different ethnic groups, are encouraged.

The Full Informational Spectral Analysis for Auditory Steady-State Responses in Human Brain Using the Combination of Canonical Correlation Analysis and Holo-Hilbert Spectral Analysis.

Auditory steady-state response (ASSR) is a translational biomarker for several neurological and psychiatric disorders, such as hearing loss, schizophrenia, bipolar disorder, autism, etc. The ASSR is sinusoidal electroencephalography (EEG)/magnetoencephalography (MEG) responses induced by periodically presented auditory stimuli. Traditional frequency analysis assumes ASSR is a stationary response, which can be analyzed using linear analysis approaches, such as Fourier analysis or Wavelet. However, recent studies have reported that the human steady-state responses are dynamic and can be modulated by the subject's attention, wakefulness state, mental load, and mental fatigue. The amplitude modulations on the measured oscillatory responses can result in the spectral broadening or frequency splitting on the Fourier spectrum, owing to the trigonometric product-to-sum formula. Accordingly, in this study, we analyzed the human ASSR by the combination of canonical correlation analysis (CCA) and Holo-Hilbert spectral analysis (HHSA). The CCA was used to extract ASSR-related signal features, and the HHSA was used to decompose the extracted ASSR responses into amplitude modulation (AM) components and frequency modulation (FM) components, in which the FM frequency represents the fast-changing intra-mode frequency and the AM frequency represents the slow-changing inter-mode frequency. In this paper, we aimed to study the AM and FM spectra of ASSR responses in a 37 Hz steady-state auditory stimulation. Twenty-five healthy subjects were recruited for this study, and each subject was requested to participate in two auditory stimulation sessions, including one right-ear and one left-ear monaural steady-state auditory stimulation. With the HHSA, both the 37 Hz (fundamental frequency) and the 74 Hz (first harmonic frequency) auditory responses were successfully extracted. Examining the AM spectra, the 37 Hz and the 74 Hz auditory responses were modulated by distinct AM spectra, each with at least three composite frequencies. In contrast to the results of traditional Fourier spectra, frequency splitting was seen at 37 Hz, and a spectral peak was obscured at 74 Hz in Fourier spectra. The proposed method effectively corrects the frequency splitting problem resulting from time-varying amplitude changes. Our results have validated the HHSA as a useful tool for steady-state response (SSR) studies so that the misleading or wrong interpretation caused by amplitude modulation in the traditional Fourier spectrum can be avoided.

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation.

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD.

Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.

BACKGROUND: Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy. METHODS: The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18-30 years), and older (31-50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400). RESULTS: We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240. CONCLUSIONS: The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition.

BACKGROUND: Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased. METHODS: This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR). RESULTS: The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (P < 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis. CONCLUSIONS: The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.

ß-Oscillations Reflect Recovery of the Paretic Upper Limb in Subacute Stroke.

Background. Recovery of upper limb function post-stroke can be partly predicted by initial motor function, but the mechanisms underpinning these improvements have yet to be determined. Here, we sought to identify neural correlates of post-stroke recovery using longitudinal magnetoencephalography (MEG) assessments in subacute stroke survivors. Methods. First-ever, subcortical ischemic stroke survivors with unilateral mild to moderate hand paresis were evaluated at 3, 5, and 12 weeks after stroke using a finger-lifting task in the MEG. Cortical activity patterns in the ß-band (16-30 Hz) were compared with matched healthy controls. Results. All stroke survivors (n=22; 17 males) had improvements in action research arm test (ARAT) and Fugl-Meyer upper extremity (FM-UE) scores between 3 and 12 weeks. At 3 weeks post-stroke the peak amplitudes of the movement-related ipsilesional ß-band event-related desynchronization (ß-ERD) and synchronization (ß-ERS) in primary motor cortex (M1) were significantly lower than the healthy controls (p<0.001) and were correlated with both the FM-UE and ARAT scores (r=0.51-0.69, p<0.017). The decreased ß-ERS peak amplitudes were observed both in paretic and non-paretic hand movement particularly at 3 weeks post-stroke, suggesting a generalized disinhibition status. The peak amplitudes of ipsilesional ß-ERS at week 3 post-stroke correlated with the FM-UE score at 12 weeks (r=0.54, p=0.03) but no longer significant when controlling for the FM-UE score at 3 weeks post-stroke.Conclusions. Although early ß-band activity does not independently predict outcome at 3 months after stroke, it mirrors functional changes, giving a potential insight into the mechanisms underpinning recovery of motor function in subacute stroke.

Steady-state auditory evoked fields reflect long-term effects of repetitive transcranial magnetic stimulation in tinnitus.

OBJECTIVES: Evidence of plastic changes in tinnitus has been demonstrated in functional brain imaging. Although repetitive transcranial magnetic stimulation (rTMS) has been shown to decrease steady-state auditory evoked fields (SSAEFs) in tinnitus, the long-term consequence remained unknown. In addition, association between plastic changes as reflected by hemispheric asymmetry and tinnitus handicap inventory (THI) before and after rTMS have not been addressed. METHODS: Twelve tinnitus patients received rTMS and 12 received sham stimulation. Another 12 healthy participants served as the normal hearing controls. Patients responded to the THI before the 1st session and at one month after the final session of rTMS/sham stimulation. Changes in brain activity were assessed by measuring SSAEFs. RESULTS: SSAEFs remained decreased one month after rTMS compared to before treatment, along with a significant reduction in THI score. There was no significant effect between the index of hemispheric asymmetry and THI score. CONCLUSIONS: The current study objectively demonstrated the long-term effects of rTMS on tinnitus using SSAEFs. A longitudinal study to develop an index using SSAEFs to assess the subjective severity of tinnitus is warranted. SIGNIFICANCE: This study suggests the possible use of SSAEFs to assess the long-term effects of rTMS on tinnitus.

Immediate gain is long-term loss: Are there foresighted decision makers in the Iowa Gambling Task?

BACKGROUND: The Somatic Marker Hypothesis suggests that normal subjects are "foreseeable" and ventromedial prefrontal patients are "myopic" in making decisions, as the behavior shown in the Iowa Gambling Task. The present study questions previous findings because of the existing confounding between long-term outcome (expected value, EV) and gain-loss frequency variables in the Iowa Gambling Task (IGT). A newly and symmetrically designed gamble, namely the Soochow Gambling Task (SGT), with a high-contrast EV between bad (A, B) and good (C, D) decks, is conducted to clarify the issue about IGT confounding. Based on the prediction of EV (a basic assumption of IGT), participants should prefer to choose good decks C and D rather than bad decks A and B in SGT. In contrast, according to the prediction of gain-loss frequency, subjects should prefer the decks A and B because they possessed relatively the high-frequency gain. METHODS: The present experiment was performed by 48 participants (24 males and 24 females). Most subjects are college students recruited from different schools. Each subject played the computer version SGT first and completed a questionnaire for identifying their final preference. The IGT experimental procedure was mostly followed to assure a similar condition of decision uncertainty. RESULTS: The SGT experiment demonstrated that the prediction of gain-loss frequency is confirmed. Most subjects preferred to choose the bad decks A and B than good decks C and D. The learning curve and questionnaire data indicate that subjects can not "hunch" the EV throughout the game. Further analysis of the effect of previous choice demonstrated that immediate gain increases the probability to stay at the same deck. CONCLUSION: SGT provides a balanced structure to clarify the confounding inside IGT and demonstrates that gain-loss frequency rather than EV guides decision makers in these high-ambiguity gambles. Additionally, the choice behavior is mostly following the "gain-stay, lose-randomize" strategy to cope with the uncertain situation. As demonstrated in SGT, immediate gain can bring about a long-term loss under uncertainty. This empirical result may explain some shortsighted behaviors in real life.

Brain computer interface using flash onset and offset visual evoked potentials.

OBJECTIVE: This paper presents a brain computer interface (BCI) actuated by flash onset and offset visual evoked potentials (FVEPs). Flashing stimuli, such as digits or letters, are displayed on a LCD screen for inducing onset and offset FVEPs when one stares at one of them. Subjects can shift their gaze at target flashing digits or letters to generate a string for communication purposes. METHODS: By designing the flickering sequences with mutually independent flash onsets (or offsets) and employing the inherent property that onset (or offset) FVEPs are time-locked and phase-locked to flash onsets (or offsets) of gazed stimuli, segmented epochs based on the flash onsets (or offsets) of gazed stimuli will be enhanced after averaging whereas those based on the onsets (or offsets) of non-gazed stimuli will be suppressed after averaging. The amplitude difference between the N2 and P2 peaks of averaged onset FVEPs, denoted by Amp(onset), and that between the N1 and P1 peaks of averaged offset FVEPs, denoted by Amp(offset), are detected during experiments. The stimulus inducing the largest value of the sum Amp(onset)+Amp(offset) is identified as the gazed target and the representative digit or letter is sent out. RESULTS: Five subjects participated in two experiments. In the first experiment, subjects were asked to gaze at 25 flickering stimuli one by one with each for a duration of 1min. The mean accuracy with 10-epoch averages was 97.4%. In the second task, subjects were instructed to generate a string '0287513694E' by staring at stimuli on a pseudo keypad comprising ten digits '0-9' and two letters 'B' and 'E'. The mean accuracy and information transfer rates were 92.18% and 33.65bits/min, respectively. CONCLUSIONS: The onset and offset FVEP-based BCI has shown that high information transfer rate has been achieved. SIGNIFICANCE: A novel FVEP-based BCI system is proposed as an efficient and reliable tool for disabled people to communicate with external environments.

Implementation of pipelined FastICA on FPGA for real-time blind source separation.

Fast independent component analysis (FastICA) algorithm separates the independent sources from their mixtures by measuring non-Gaussian. FastICA is a common offline method to identify artifact and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement FastICA for real-time signal processing. In this paper, the FastICA algorithm is implemented in a field-programmable gate array (FPGA), with the ability of real-time sequential mixed signals processing by the proposed pipelined FastICA architecture. Moreover, in order to increase the numbers precision, the hardware floating-point (FP) arithmetic units had been carried out in the hardware FastICA. In addition, the proposed pipeline FastICA provides the high sampling rate (192 kHz) capability by hand coding the hardware FastICA in hardware description language (HDL). To verify the features of the proposed hardware FastICA, simulations are first performed, then real-time signal processing experimental results are presented using the fabricated platform. Experimental results demonstrate the effectiveness of the presented hardware FastICA as expected.

Analyses of EEG Oscillatory Activities during Slow and Fast Repetitive Movements using Holo-Hilbert Spectral Analysis.

Neural oscillatory activities existing in multiple fre-quency bands usually represent different levels of neurophysiolog-ical meanings, from micro-scale to macro-scale organizations. In this study, we adopted Holo-Hilbert spectral analysis (HHSA) to study the amplitude-modulated (AM) and frequency-modulated (FM) components in sensorimotor Mu rhythm, induced by slow- and fast-rate repetitive movements. The HHSA-based approach is a two-layer empirical mode decomposition (EMD) architecture, which firstly decomposes the EEG signal into a series of frequency-modulated intrinsic mode functions (IMF) and then decomposes each frequency-modulated IMF into a set of amplitude-modulated IMFs. With the HHSA, the FM and AM components were incor-porated with their instantaneous power to achieve full-informa-tional spectral analysis. We observed that the instantaneous power induced by slow-rate movements was significantly higher than that induced by fast-rate movements (p < 0.01, Wilcoxon signed rank test). The alpha-band AM frequencies induced by slow-rate movements were higher than those induced by fast-rate move-ments, while no statistical difference was found in beta-band AM frequencies. In addition, to study the functional coupling between the primary sensorimotor area and other brain regions, spectral coherence was applied and statistical difference was found in frontal area in slow-rate versus fast-rate movements. The discrep-ancy between slow- and fast-rate movements might be owing to the change of motor functional modes from default mode network (DMN) to automatic timing with the increase of movement rates. The use of HHSA for oscillatory activity analysis can be an effi-cient tool to provide informative interaction among different fre-quency bands.

Is deck B a disadvantageous deck in the Iowa Gambling Task?

BACKGROUND: The Iowa gambling task is a popular test for examining monetary decision behavior under uncertainty. According to Dunn et al. review article, the difficult-to-explain phenomenon of "prominent deck B" was revealed, namely that normal decision makers prefer bad final-outcome deck B to good final-outcome decks C or D. This phenomenon was demonstrated especially clearly by Wilder et al. and Toplak et al. The "prominent deck B" phenomenon is inconsistent with the basic assumption in the IGT; however, most IGT-related studies utilized the "summation" of bad decks A and B when presenting their data, thereby avoiding the problems associated with deck B. METHODS: To verify the "prominent deck B" phenomenon, this study launched a two-stage simple version IGT, namely, an AACC and BBDD version, which possesses a balanced gain-loss structure between advantageous and disadvantageous decks and facilitates monitoring of participant preferences after the first 100 trials. RESULTS: The experimental results suggested that the "prominent deck B" phenomenon exists in the IGT. Moreover, participants cannot suppress their preference for deck B under the uncertain condition, even during the second stage of the game. Although this result is incongruent with the basic assumption in IGT, an increasing number of studies are finding similar results. The results of the AACC and BBDD versions can be congruent with the decision literatures in terms of gain-loss frequency. CONCLUSION: Based on the experimental findings, participants can apply the "gain-stay, loss-shift" strategy to overcome situations involving uncertainty. This investigation found that the largest loss in the IGT did not inspire decision makers to avoid choosing bad deck B.