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2.A.1.1.3.8.-Neurofeedback -Biofeedback-
-----------------------------------------------Alejandro Gustavo Sabatini
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Este archivo está en construcción:
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NEUROFEEBACK:
Estudios de investigación han demostrado la utilidad del Neurofeedback en el Trastorno por Déficit de Atención con Hiperactividad. Asimismo se halla en fase de investigación para saber si es de utilidad, o no, en los siguientes casos:
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16:47 Posted in Biofeedback & neurofeedback, Brain-computer interface | Permalink | Comments (0) | Email this | Tags: brain-computer interface
_______________________________________________________________________________________________________________
Links:
http://ispr.info/
International Society for Neurofeeback & Research
http://www.isnr.org/
Neurofeedback
http://www.neurofeedback.com.mx/
Neurobit System
http://www.neurobitsystems.com/es/neurofeedback-faq.htm
http://blogs.nature.com/nn/actionpotential/
http://www.dana.org/
http://www.nature.com/neurosci/neuropod/index.html__________________________________________________________________________________________________
SITEMAP http://interfacemindbraincomputer.wetpaint.com/sitemap
-------------------------------------------------------
Este archivo está en construcción:___________________________________________________________________________________
NEUROFEEBACK:
Estudios de investigación han demostrado la utilidad del Neurofeedback en el Trastorno por Déficit de Atención con Hiperactividad. Asimismo se halla en fase de investigación para saber si es de utilidad, o no, en los siguientes casos:
- Deterioro o déficit cognitivo (atención, concentración y memoria)
- Ansiedad
- Depresión
- Trastornos del Sueño
- Adicciones y Alcoholismo
- Demencias de grado leve
- Impulsividad (Trastorno del Control de los Impulsos)
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Jun 09, 2009
Neurofeedback-based motor imagery training for brain-computer interface
Neurofeedback-based motor imagery training for brain-computer interface (BCI).
J Neurosci Methods. 2009 Apr 30;179(1):150-156
Authors: Hwang HJ, Kwon K, Im CH
In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.
J Neurosci Methods. 2009 Apr 30;179(1):150-156
Authors: Hwang HJ, Kwon K, Im CH
In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.
19:59 Posted in Biofeedback & neurofeedback, Mental practice & mental simulation | Permalink | Comments (0) | Trackbacks (0) | Email this | Tags: brain-computer interface, neurofeedback, motor imagery
May 03, 2009
QEEG guided neurofeedback therapy in personality disorders
QEEG guided neurofeedback therapy in personality disorders: 13 case studies. Clin EEG Neurosci. 2009 Jan;40(1):5-10 Authors: Surmeli T, Ertem A According to DSM-IV, personality disorder constitutes a class only when personality traits are inflexible and maladaptive and cause either significant functional impairment or subjective distress. Classical treatment of choice for personality disorders has been psychotherapy and/or psychopharmacotherapy. Our study is to determine if subjects with antisocial personality disorders will benefit from quantitative EEG (qEEG) guided neurofeedback treatment. Thirteen subjects (9 male, 4 female) ranged in age from 19 to 48 years. All the subjects were free of medications and illicit drugs. We excluded subjects with other mental disorders by clinical assessment. Psychotherapy or psychopharmacotherapy or any other treatment model was not introduced to any of the subjects during or after neurofeedback treatment. For the subject who did not respond to neurofeedback, training was applied with 38 sessions of LORETA neurofeedback training without success. Evaluation measures included qEEG analysis with Nx Link data base, MMPI, T.O.V.A tests and SA-45 questionaries at baseline, and at the end of neurofeedback treatment. Lexicor qEEG signals were sampled at 128 Hz with 30 minutes-neurofeedback sessions completed between 80-120 sessions depending on the case, by Biolex neurofeedback system. At baseline and after every 20 sessions, patients were recorded with webcam during the interview. Twelve out of 13 subjects who received 80-120 sessions of neurofeedback training showed significant improvement based on SA-45 questionaries, MMPI, T.O.V.A. and qEEG/Nx Link data base (Neurometric analysis) results, and interviewing by parent/family members. Neurofeedback can change the view of psychiatrists and psychologists in the future regarding the treatment of personality disorders. This study provides the first evidence for positive effects of neurofeedback treatment in antisocial personality disorders. Further study with controls is warranted.
21:36 Posted in Biofeedback & neurofeedback | Permalink | Comments (1) | Email this | Tags: biofeedback, neurofeedback
Oct 22, 2008
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.
PLoS Comput Biol. 2008 Oct;4(10):e1000180
Authors: Legenstein R, Pecevski D, Maass W
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics.
PLoS Comput Biol. 2008 Oct;4(10):e1000180
Authors: Legenstein R, Pecevski D, Maass W
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics.
19:18 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback
Jul 28, 2008
Development and preliminary evaluation of a prototype audiovisual biofeedback device
Development and preliminary evaluation of a prototype audiovisual biofeedback device incorporating a patient-specific guiding waveform. Phys Med Biol. 2008 May 12;53(11):N197-N208 Authors: Venkat RB, Sawant A, Suh Y, George R, Keall PJ The aim of this research was to investigate the effectiveness of a novel audio-visual biofeedback respiratory training tool to reduce respiratory irregularity. The audiovisual biofeedback system acquires sample respiratory waveforms of a particular patient and computes a patient-specific waveform to guide the patient's subsequent breathing. Two visual feedback models with different displays and cognitive loads were investigated: a bar model and a wave model. The audio instructions were ascending/descending musical tones played at inhale and exhale respectively to assist in maintaining the breathing period. Free-breathing, bar model and wave model training was performed on ten volunteers for 5 min for three repeat sessions. A total of 90 respiratory waveforms were acquired. It was found that the bar model was superior to free breathing with overall rms displacement variations of 0.10 and 0.16 cm, respectively, and rms period variations of 0.77 and 0.33 s, respectively. The wave model was superior to the bar model and free breathing for all volunteers, with an overall rms displacement of 0.08 cm and rms periods of 0.2 s. The reduction in the displacement and period variations for the bar model compared with free breathing was statistically significant (p = 0.005 and 0.002, respectively); the wave model was significantly better than the bar model (p = 0.006 and 0.005, respectively). Audiovisual biofeedback with a patient-specific guiding waveform significantly reduces variations in breathing. The wave model approach reduces cycle-to-cycle variations in displacement by greater than 50% and variations in period by over 70% compared with free breathing. The planned application of this device is anatomic and functional imaging procedures and radiation therapy delivery.
13:17 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback, neurofeedback
Energetic assessment of trunk postural modifications induced by a wearable audio-biofeedback system
Energetic assessment of trunk postural modifications induced by a wearable audio-biofeedback system.
Med Eng Phys. 2008 Jul 2;
Authors: Giansanti D, Dozza M, Chiari L, Maccioni G, Cappello A
Med Eng Phys. 2008 Jul 2;
Authors: Giansanti D, Dozza M, Chiari L, Maccioni G, Cappello A
This paper investigates the trunk postural modifications induced by a wearable device which assesses the trunk sway and provides biofeedback information through sonification of trunk kinematics. The device is based on an inertial wearable sensing unit including three mono-axial accelerometers and three rate gyroscopes embedded and mounted orthogonally. The biofeedback device was tested on nine healthy subjects during quiet stance in different conditions of sensory limitation eyes closed on solid surface, eyes open on foam cushion surface, eyes closed on foam cushion surface. Five trials were performed for each condition; the order of the trials was randomized. The results reported in this paper show how subjects reduced their rotational kinetic energy by using the biofeedback information and how this reduction was related to the limitation of sensory information.
13:16 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback, neurofeedback
Apr 23, 2008
The effect of biofeedback training on affective regulation and simulated car-racing performance
The effect of biofeedback training on affective regulation and simulated car-racing performance: A multiple case study analysis. J Sports Sci. 2008 May;26(7):761-73 Authors: Edmonds WA, Tenenbaum G, Mann DT, Johnson M, Kamata A The foundation of this study was based on an idiosyncratic concept, which uses probabilistic determinations (Kamata, Tenenbaum, & Hanin, 2002) to verify the utility and effectiveness of a biofeedback intervention by manipulating affective performance states in a race-car simulator. Nine males completed five separate time-trials of a simulated racing task and were then randomly assigned to one of three arousal regulation treatment conditions: (1) optimal, (2) poor, and (3) attention control. Following the biofeedback intervention, participants underwent another series of race trials to determine the effectiveness of the arousal regulation intervention. The results indicated that there were relative similarities in the strength and direction of the perceived and physiological states between the participants; however, the subtle details of the participants' unique performance zones and the probability of achieving each zone were revealed to be unique among the participants. The results also indicated that: (a) the biofeedback manipulation resulted in the expected changes for each participant, and (b) there were some large individual differences among the participants, necessitating the idiosyncratic approach. Limitations and future directions are also addressed.
23:08 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback
Apr 11, 2008
Alpha neurofeedback improves the maintaining ability of alpha activity
Alpha neurofeedback improves the maintaining ability of alpha activity.
Neuroreport. 2008 Feb 12;19(3):315-7
Authors: Cho MK, Jang HS, Jeong SH, Jang IS, Choi BJ, Lee MG
Neuroreport. 2008 Feb 12;19(3):315-7
Authors: Cho MK, Jang HS, Jeong SH, Jang IS, Choi BJ, Lee MG
The effects of alpha-neurofeedback (ANF) on electroencephalographic alpha-activity were investigated. Each session consisted of a 2.5-min eye-opened state and 17.5-min of ANF, which was divided into 16 1.25-min bins. Alpha amplitudes were gradually increased as the session was repeated. The maximum value at the start of ANF gradually decreased as time passed, but the slowdown of alpha-activity during each session was decreased as the session was repeated. The correlation between alpha-activity at the end of ANF and at the following session's eye-opened state was highly significant. These results showed that ANF enhances the ability of alpha-activity to maintain itself rather than the increase of alpha-amplitude during intrasession and that the maintained alpha-activity during former training remained until the next session.
18:29 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback, neurofeedback
Mar 03, 2008
Integrating a Portable Biofeedback Device into Clinical Practice for Patients with Anxiety Disorders
Integrating a Portable Biofeedback Device into Clinical Practice for Patients with Anxiety Disorders: Results of a Pilot Study.
Appl Psychophysiol Biofeedback. 2008 Feb 20;
Authors: Reiner R
This study examined the effectiveness of a portable Respiratory Sinus Arrhythmia (RSA) biofeedback device as an adjunct to CBT in persons with anxiety disorders and other disorders associated with autonomic dysfunction attending outpatient treatment. Participants were 24 individuals attending outpatient cognitive behavioral treatment for a range of anxiety disorders. Participants were assessed over a 3 week period. Outcomes included measures of anxiety (STAI-Y), sleep disturbances (PSQI), anger (STAEI), and subjective questions about the effectiveness of the device as a treatment adjunct. Significant reductions were found for anxiety and anger and for certain sleep variables (e.g. sleep latency). There was a significant dos-effect in that those who were more compliant had significantly greater reductions in most domains including sleep, anger and trait anxiety. Overall, participants found the device more helpful than other relaxation techniques such as mediation, yoga and unassisted breathing techniques but less helpful than exercise. The most frequently endorsed side effects were dizziness (15%) and sleepiness (55%). These preliminary results suggest that portable RSA biofeedback appears to be a promising treatment adjunct for disorders of autonomic arousal and is easily integrated into treatment. Results support the need for further investigation with more rigorous experimental designs.
Appl Psychophysiol Biofeedback. 2008 Feb 20;
Authors: Reiner R
This study examined the effectiveness of a portable Respiratory Sinus Arrhythmia (RSA) biofeedback device as an adjunct to CBT in persons with anxiety disorders and other disorders associated with autonomic dysfunction attending outpatient treatment. Participants were 24 individuals attending outpatient cognitive behavioral treatment for a range of anxiety disorders. Participants were assessed over a 3 week period. Outcomes included measures of anxiety (STAI-Y), sleep disturbances (PSQI), anger (STAEI), and subjective questions about the effectiveness of the device as a treatment adjunct. Significant reductions were found for anxiety and anger and for certain sleep variables (e.g. sleep latency). There was a significant dos-effect in that those who were more compliant had significantly greater reductions in most domains including sleep, anger and trait anxiety. Overall, participants found the device more helpful than other relaxation techniques such as mediation, yoga and unassisted breathing techniques but less helpful than exercise. The most frequently endorsed side effects were dizziness (15%) and sleepiness (55%). These preliminary results suggest that portable RSA biofeedback appears to be a promising treatment adjunct for disorders of autonomic arousal and is easily integrated into treatment. Results support the need for further investigation with more rigorous experimental designs.
23:21 Posted in Biofeedback & neurofeedback | Permalink | Comments (0) | Email this | Tags: biofeedback
Nov 04, 2007
Using movement imagery and electromyography-triggered feedback in stroke rehabilitation
Effects of movement imagery and electromyography-triggered feedback on arm hand function in stroke patients in the subacute phase. Clin Rehabil. 2007 Jul;21(7):587-94 Authors: Hemmen B, Seelen HA OBJECTIVE: To investigate the effects of movement imagery-assisted electromyography (EMG)-triggered feedback (focused on paretic wrist dorsiflexors) on the arm-hand function of stroke patients. DESIGN: Single-blinded, longitudinal, multicentre randomized controlled trial. Measurements were performed (on average) 54 days post stroke (baseline), three months later (post training) and at 12 months post baseline. SETTING: Two rehabilitation centres. SUBJECTS: Twenty-seven patients with a first-ever, ischaemic, subacute stroke. INTERVENTIONS: A reference group received conventional electrostimulation, while the experimental group received arm-hand function training based on EMG-triggered feedback combined with movement imagery. Both groups were trained for three months, 5 days/week, 30 minutes/day, in addition to their therapy as usual. MAIN MEASURES: Arm-hand function was evaluated using the upper extremity-related part of the Brunnstrom Fugl-Meyer test and the Action Research Arm test. RESULTS: During training, Brunnstrom Fugl-Meyer scores improved 8.7 points and Action Research Arm scores by 19.4 points (P < 0.0001) in both groups relative to baseline results, rising to 13.3 and 28.4 points respectively at one year follow-up (P < 0.0001). No between-group differences were found at any time. CONCLUSIONS: EMG-triggered feedback stimulation did not lead to more arm-hand function improvement relative to conventional electrostimulation. However, in contrast to many clinical reports, a significant improvement was still observed in both groups nine months after treatment ceased.
12:35 Posted in Biofeedback & neurofeedback, Mental practice & mental simulation | Permalink | Comments (0) | Email this | Tags: mental practice
Oct 20, 2007
A brain-computer interface with vibrotactile biofeedback for haptic information
A brain-computer interface with vibrotactile biofeedback for haptic information. J Neuroengineering Rehabil. 2007 Oct 17;4(1):40 Authors: Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV ABSTRACT: BACKGROUND: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. METHODS: A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. RESULTS AND CONCLUSIONS: Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.
_______________________________________________________________________________________________________________
Links:
http://ispr.info/
International Society for Neurofeeback & Research
http://www.isnr.org/
Neurofeedback
http://www.neurofeedback.com.mx/
Neurobit System
http://www.neurobitsystems.com/es/neurofeedback-faq.htm
http://blogs.nature.com/nn/actionpotential/
http://www.dana.org/
http://www.nature.com/neurosci/neuropod/index.html__________________________________________________________________________________________________
SITEMAP http://interfacemindbraincomputer.wetpaint.com/sitemap
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