Sensorimotor Rhythm Amplitude Research Articles

Subjective Improvement of Sleep in Insomnia Patients Treated at a Day Rehabilitation Centre After the Use of EEG Neurofeedback Therapy – a Pilot Study

Introduction: Insomnia affects nearly 1/3 of the worldwide population. Electroencephalography neurofeedback (EEG-NFB) is one of the methods used in applied psychophysiology, which can improve nightly sleep scheme. Research objective: The aim of this pilot study was to assess the relative effect of a 20-day neurorehabilitation intervention based on EEG-NFB therapy in insomnia patients treated at a day rehabilitation centre. Materials and methods: Seventy-four patients with insomnia: 28 women (mean age ± SD: 67.9 ± 8.84 years, range: 42–83 years) and 46 men (mean age ± SD: 63.0 ± 9.24 years, range: 42-80 years) were subjected to the EEG-NFB training-neurorehabilitation using the C4 protocol: sensorimotor rhythm (SMR) (12-15 Hz)/theta (4-7 Hz). The individual everyday EEG-NFB training consisted of 20, 30-minute sessions. Before and after the training, the data was collected from 12-electrode quantitative EEG (QEEG) tests. In addition, several standardised psychological questionnaires were performed: Pittsburgh Sleep Quality Index (PSQI), State-Trait Anxiety Inventory (STAI) and Beck Depression Inventory (BDI). Results: The EEG-NFB therapy reduced anxiety (7.39 ± 1.0 vs. 6.12 ± 0.88 in STAI, p< 0.001) and improved patients' mood (17.6 ± 3.9 vs. 14.65 ± 3.39 in BDI, p< 0.001). During the PSQI test, the time of falling asleep and number of night awakenings were statistically reduced (both p<0.001). However, there was no significant difference in the -SMR amplitude between pre- to post-treatment (9.15 ± 3.11 and 8.62 ± 2.82, respectively, p=0.095). Conclusions: Due to the subjective improvement of sleep quality, without statistically significant changes in the electrophysiological record (expressed by SMR amplitude), it is advisable to continue research with the use of EEG-NFB therapy.

Enhancing the Effects of Neurofeedback Training: The Motivational Value of the Reinforcers.

The brain activity that is measured by electroencephalography (EEG) can be modified through operant conditioning, specifically using neurofeedback (NF). NF has been applied to several disorders claiming that a change in the erratic brain activity would be accompanied by a reduction of the symptoms. However, the expected results are not always achieved. Some authors have suggested that the lack of an adequate response may be due to an incorrect application of the operant conditioning principles. A key factor in operant conditioning is the use of reinforcers and their value in modifying behavior, something that is not always sufficiently taken into account. This work aims to clarify the relevance of the motivational value versus the purely informational value of the reinforcer. In this study, 113 subjects were randomly assigned two different reinforcer conditions: a selected reinforcer—the subjects subjectively selected the reinforcers—or an imposed reinforcer—the reinforcers were assigned by the experimenter—and both groups undertook NF sessions to enhance the sensorimotor rhythm (SMR). In addition, the selected reinforcer group was divided into two subgroups: one receiving real NF and the other one sham NF. There were no significant differences between the groups at baseline in terms of SMR amplitude. After the intervention, only those subjects belonging to the selected reinforcer group and receiving real NF increased their SMR. Our results provide evidence for the importance of the motivational value of the reinforcer in Neurofeedback success.

BCI-based sensorimotor rhythm training can affect individuated finger movements

ABSTRACT Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. BCI technology might also be able to enhance rehabilitation of motor function. We have previously shown that pre-movement sensorimotor rhythm (SMR) amplitude affects reaction time and performance on a joystick-based cursor movement task. The present study explores in adults without motor impairment the possibility that pre-movement SMR amplitude affects performance of individuated finger movements. In Phase 1, 8 individuals performed a finger flexion task that was monitored by an exoskeleton. During a 1-sec preparatory period, two colored targets on a video monitor cued flexion of the index finger, middle finger, both fingers, or neither finger; sudden color change then triggered the movement (or non-movement). SMR features (i.e. EEG amplitudes in specific frequency bands at specific scalp locations) in the pre-movement EEG that correlated with movement versus no movement were identified. In Phase 2, the participants learned to increase or decrease these SMR features to control a two-target BCI task. Finally, in Phase 3, they were asked to increase or decrease the SMR features to initiate the finger flexion task of Phase 1 and the impact on finger flexion performance was assessed. After BCI training, pre-movement SMR feature amplitude affected performance in a subset of individuals: lower amplitude was associated with shorter movement onset. In a subset of individuals, the beneficial effect on performance of lower SMR amplitude was greater when both fingers were flexed than when one was flexed and the other remained extended; thus, the impact of SMR amplitude modulation depended on the specificity of the subsequent motor task. These results indicate that BCI-based training of SMR activity in the pre-movement preparatory period can affect finger movements in a subset of individuals. They encourage studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful hand function.

Investigation of the effects of transcranial direct current stimulation and neurofeedback by continuous performance test

Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technique based on weak direct current stimulation through the scalp. Neurofeedback (NFB) is a learning strategy that may help alter to brain wave parameters, by monitoring electroencephalography (EEG) feedback via special programs. We aimed to investigate the supportive effects of tDCS in addition to NFB training. 16 healthy volunteers were divided equally into two groups. One of the groups was trained by NFB with the sensorimotor rhythm (SMR) protocol; 2 days per week, 10 sessions of 30 min, the other group received 10 min of tDCS before each NFB sessions. Continuous Performance Test (CPT) was used to measure, response time and suppression and to determine selective attention condition. Also, Beck Depression and Anxiety Inventories were used to exclude people with depression and anxiety. Depression scores of NFB + tDCS group were decreased significantly. CPT scores were better at last sessions for both groups compared to the first sessions. Sessions were analyzed by comparing 1st, 2nd, 5th and 10th sessions. While the NFB + tDCS group had statistically significant changes at theta/beta ratios with SMR and alpha band amplitudes, NFB group statistics had changed at theta/SMR ratios. NFB training shows its effects at the end of 10 sessions. Despite an increase in the latencies of correct and commission responses on the task of CPT, additional use of tDCS improves cognitive performance. Also, tDCS has a supportive effect on the healthy participants who have mild anxiety and depression; also inhibition deficits of subjects were clear.

Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke

Objective. Brain–computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. Approach. Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. Main results. Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3 ± 7.5 blocks versus 3.5 ± 3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. Significance. These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.

Neuromodulating attention and mind-wandering processes with multi-session real-time electroencephalogram.

Previous studies showed the efficacy of a single session real-time electroencephalogram (rtEEG) protocols in sensorimotor rhythm (SMR) or Theta up-training. However, the impact of this training on sustained or mind-wandering attention was only modest. This could be explained by the lack of specificity in distinct rtEEG training protocols given their limitation in inhibiting or decreasing the amplitude of down-trained bands. Additionally, multiple sessions of rtEEG in up-training/down-training SMR (sensorimotor rhythm) and Theta along with better ways of tracking sustained and mind-wandering attention protocols may be required to achieve consistent effects. Here we describe the effects of a 10-session trial of up-training/down-training SMR or Theta (SMR⇑Theta⇓; Theta⇑SMR⇓), looking at the effects of 2 rtEEG training protocols in 2 n of 1 subject designs. We also tested to impact of this training in sustained and mind-wandering attention during the course of a Sustained Attention Response Task (SART). The present trial confirmed the potentiality of a multi-session protocol in up-training Theta or SMR and, as consequence, increasing sustained attention (Theta up-training) and mind-wandering attention (SMR up-training). However, the simultaneous increase of the Theta amplitude in the SMR⇑Theta⇓ (and the more modest increase of the SMR amplitude in Theta⇑SMR⇓) reduced the specificity of the rtEEG training. Future studies should build on the potentiality of extended rtEEG protocols on this attention paradigm but increasing the specificity of the trained EEG bands choosing less tedious/more motivating feedback instruments (SMR⇑Theta⇓) and conducting the Theta⇑SMR⇓ training eyes closed.

Neurofeedback of SMR and Beta1 Frequencies: An Investigation of Learning Indices and Frequency-Specific Effects

Despite evidence that Sensorimotor Rhythm (SMR) and beta1 neurofeedback have distinct cognitive enhancement effects, it remains unclear whether their amplitudes can be independently enhanced. Furthermore, demands for top-down attention control, postural restraint and maintenance of cognitive set processes, all requiring low-beta frequencies, might masquerade as learning and confound interpretation. The feasibility of selectively enhancing SMR and beta1 amplitudes was investigated with the addition of a random frequency control condition that also requires the potentially confounding cognitive processes. A comprehensive approach to assessing neurofeedback learning was undertaken through the calculation of learning indices within- and across-session and pre-to-post baseline. Herein we provide the first demonstration of beta1 within-session amplitude learning that was not attributable to extraneous cognitive processes, for it was not found with random frequency training. On the other hand, within-session SMR learning might have been obscured by high interindividual variability and methodological limitations such as the type of feedback screen, the insufficient number of sessions, and the exclusion of simultaneous theta and high-beta inhibition. Interestingly, SMR and beta1 amplitude increased across sessions in the three groups suggesting unspecific effects of neurofeedback in the low beta frequency band. Moreover, there was no clear evidence of frequency specificity associated with either SMR or beta1 training. Some methodological limitations may underpin the divergent results with previous studies.

Effects of training pre-movement sensorimotor rhythms on behavioral performance

Objective. Brain–computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects. Approach. Eight individuals first performed a joystick-based cursor-movement task with variable warning periods. Targets appeared randomly on a video monitor and subjects moved the cursor to the target and pressed a select button within 2 s. SMR features in the pre-movement EEG that correlated with performance speed and accuracy were identified. The subjects then learned to increase or decrease these features to control a two-target BCI task. Following successful BCI training, they were asked to increase or decrease SMR amplitude in order to initiate the joystick task. Main results. After BCI training, pre-movement SMR amplitude was correlated with performance in subjects with initial poor performance: lower amplitude was associated with faster and more accurate movement. The beneficial effect on performance of lower SMR amplitude was greater in subjects with lower initial performance levels. Significance. These results indicate that BCI-based SMR training can affect a standard motor behavior. They provide a rationale for studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful motor function.

Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery

Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain–computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

Neurofeedback Training for Tourette Syndrome: An Uncontrolled Single Case Study

Gilles de la Tourette syndrome (TS) is characterized by motor and vocal tic manifestations, often accompanied by behavioral, cognitive and affective dysfunctions. Electroencephalography of patients with TS has revealed reduced Sensorimotor Rhythm (SMR) and excessive fronto-central Theta activity, that presumably underlie motor and cognitive disturbances in TS. Some evidence exists that neurofeedback (NFB) training aimed at enhancing SMR amplitude is effective for reducing tics. The present report is an uncontrolled single case study where a NFB training protocol, involving combined SMR uptraining/Theta downtraining was delivered to a 17-year-old male with TS. After sixteen SMR-Theta sessions, six additional sessions were administered with SMR uptraining alone. SMR increase was better obtained when SMR uptraining was administered alone, whereas Theta decrease was observed after both trainings. The patient showed a reduction of tics and affective symptoms, and improvement of cognitive performance after both trainings. Overall, these findings suggest that Theta decrease might account for some clinical effects seen in conjunction with SMR uptraining. Future studies should clarify the feasibility of NFB protocols for patients with TS beyond SMR uptraining alone.

A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain–computer interface

People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain–computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.

Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain–computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.

Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance

People can learn to control electroencephalogram (EEG) features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. In the standard one-dimensional application, the cursor moves horizontally from left to right at a fixed rate while vertical cursor movement is continuously controlled by sensorimotor rhythm amplitude. The right edge of the screen is divided among 2-6 targets, and the user's goal is to control vertical cursor movement so that the cursor hits the correct target when it reaches the right edge. Up to the present, vertical cursor movement has been a linear function of amplitude in a specific frequency band [i.e., 8-12 Hz (mu) or 18-26 Hz (beta)] over left and/or right sensorimotor cortex. The present study evaluated the effect of controlling cursor movement with a weighted combination of these amplitudes in which the weights were determined by an regression algorithm on the basis of the user's past performance. Analyses of data obtained from a representative set of trained users indicated that weighted combinations of sensorimotor rhythm amplitudes could support cursor control significantly superior to that provided by a single feature. Inclusion of an interaction term further improved performance. Subsequent online testing of the regression algorithm confirmed the improved performance predicted by the offline analyses. The results demonstrate the substantial value for brain-computer interface applications of simple multivariate linear algorithms. In contrast to many classification algorithms, such linear algorithms can easily incorporate multiple signal features, can readily adapt to changes in the user's control of these features, and can accommodate additional targets without major modifications.

Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans

People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation ( r 2) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.