Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance

doi:10.1016/j.neuroimage.2010.08.078

NeuroImage

Volume 54, Issue 2, 15 January 2011, Pages 1427-1431

https://doi.org/10.1016/j.neuroimage.2010.08.078 Get rights and content

Abstract

In this study, the individually determined upper alpha frequency band in EEG (electroencephalogram) was investigated as a neurofeedback parameter. Fourteen subjects were trained on five sessions within 1 week by means of feedback dependent on the current upper alpha amplitude. On the first and fifth session, cognitive ability was tested by a mental rotation test. As a result, eleven of the fourteen subjects showed significant training success. Individually determined upper alpha was increased independently of other frequency bands. The enhancement of cognitive performance was significantly larger for the neurofeedback group than for a control group who did not receive feedback. Thus, enhanced cognitive control went along with an increased upper alpha amplitude that was found in the neurofeedback group only.

Research Highlights

►Human EEG oscillations in the upper alpha frequency band are enhanced by neurofeedback. ►EEG-enhancement was accompanied by an improvement of performance in a cognitive task. ►EEG-enhancement due to neurofeedback was restricted to the frequency range that was used for feedback.

Introduction

Recently, the significant role of oscillations for brain functions and behavior as well as for psychiatric diseases became increasingly obvious (Basar & Güntekin, 2008, Basar et al., 2000, Herrmann & Knight, 2001, Herrmann et al., 2004, Strüber & Herrmann, 2002, Uhlhaas & Singer, 2006). Neurofeedback training (NFT) as an operant conditioning method to control oneself's brain activity has been shown to be an appropriate way to control or change these oscillations. In addition to clinical treatment for attention-deficit hyperactivity disorder (Lubar, 2003), epilepsy (Sterman, 2000) and other disorders (Saxby & Peniston, 1995, Gruzelier et al., 1999, Hardt & Kamiya, 1978), NFT is applied to train locked-in patients to communicate (Birbaumer et al., 1999). It even seems to have effects on certain performance measures like semantic working memory (Vernon et al., 2003) and mental rotation ability (Hanslmayr et al., 2005; see Vernon, 2005, for an overview).

But the application of NFT has also received criticism concerning the reliability of its effects. Egner et al. (2004) addressed one main problem that–despite of significant behavioral impact–sometimes no spectral effects can be found after NFT. In that study, when effects with regard to the EEG were observed at all, they often were unreliable or did not meet expectations concerning the frequency spectrum or the topography (see also the tables in Vernon, 2005).

We propose three criteria for the validation of a neurofeedback parameter: There should be spectral effects within the trained frequency band caused by the training (trainability). These spectral changes should not affect other frequency bands (independence). Finally, it is reasonable to choose a frequency band that is associated with certain cognitive functions to increase the probability of reliable behavioral effects as well as applicability (interpretability).

Many of the existing studies on NFT parameters do not satisfy all three criteria. The alpha band (8–12.5 Hz), for example, has been shown to be increased after NFT (Bauer, 1976), but for the frequency range tested there, no cognitive changes could be demonstrated. In a recently published article, Cho et al. (2008) reported an increased amplitude in the same frequency band, but possible effects on cognitive performance were not examined. Other studies concerning the impact of non-clinical NFT of SMR (sensory-motor rhythm, 12–15 Hz) on EEG and performance either found no effects or found effects but at unexpected frequencies or electrodes (e.g., Vernon, 2005, Egner et al., 2004).

Constraining alpha to the individually determined upper alpha band (ranging from the individual alpha frequency, IAF, to IAF+2 Hz) might cause an improvement of trainability (Hanslmayr et al., 2005). However, this was shown only within 1 day of NFT—a possible long-term effect was not examined. There is evidence of at least two independent (lower and upper) alpha subbands (Angelakis & Lubar, 2002, Klimesch et al., 1997, Michels et al., 2008). Finally, upper alpha is widely shown to be correlated with cognitive performance (for a review, see Klimesch, 1999), indicating interpretability. For instance, prestimulus activity in the UA band has been shown to be positively related to performance in mental rotation tasks (Hanslmayr et al., 2005, Klimesch et al., 2003).

Despite these promising results, the usability of upper alpha (UA) for NFT and its spectral as well as cognitive impact for longer periods of time are hardly investigated yet. Thus, in the current study the trainability of the UA band and its impact on cognitive abilities were examined. Our hypotheses were (i) an increase of the UA amplitude, (ii) being independent of other frequency bands, and (iii) being related to improved performance.

Section snippets

Design

For each subject, the experiment consisted of five sessions within the same week from Monday to Friday with one training session each day. Within one session, the structure was the following (Fig. 1A):

At first, a short EOG (electrooculogram) calibration measurement (not depicted) was conducted for subsequent detection of eye-movement artefacts. Then, a base rate of 5 min was recorded. Afterwards, five training blocks of 5 min each, followed by a second base rate measurement, were acquired. On

Trainability

The average IAF was 9.2 ± 0.91 Hz during the first base rate and 9.16 ± 0.76 Hz during the second base rate. No significant changes were observed in the course of the training.

In Fig. 1B, the average UA amplitudes across all responders are shown as a function of the temporal course of the study. Trainability is reflected by the positive slope of the UA amplitudes over the course of time. The gradients of a fitted regression line for each subject (n = 11) are significantly larger than zero (t(10) = 3.18, p

Discussion

In line with our hypotheses, the trainability of the UA band (hypothesis i) has been confirmed: All but three of the fourteen subjects of the NFT group showed significant training success. This is remarkable, since in therapy usually a ten-fold higher number of sessions is used (Lubar, 2003).

In the course of the week, a linear increase of the UA amplitude was visible (Fig. 1B). Also in between sessions the training effects remained present over an extended period of time. Even before training,

Conclusions

Our study revealed promising results for the usage of individually determined UA as a NFT frequency parameter, fulfilling the criteria of trainability, independence, and interpretability, thus inspiring further examinations of the interactions and relations between UA, NFT and cognitive performance.

References (35)

E. Basar et al.
A review of brain oscillations in cognitive disorders and the role of neurotransmitters
Brain Res.
(2008)
E. Basar et al.
Brain oscillations in perception and memory
Int. J. Psychophysiol.
(2000)
R.H. Bauer
Short-term memory: EEG alpha correlates and the effect of increased alpha
Behav. Biol.
(1976)
M. Doppelmayr et al.
Intelligence related differences in EEG-bandpower
Neurosci. Lett.
(2005)
T. Egner et al.
The effects of neurofeedback training on the spectral topography of the electroencephalogramm
Clin. Neurophysiol.
(2004)
A. Fink
Event-related desynchronisation in the EEG during emotional and cognitive information processing: differential effects of extraversion
Biol. Psychol.
(2005)
A. Fink et al.
EEG alpha band dissociation with increasing task demands
Brain Res. Cogn. Brain Res.
(2005)
J. Gruzelier et al.
Critical validation studies of neurofeedback
Child Adolesc. Psychiatr. Clin. N. Am.
(2005)
J. Gruzelier et al.
Learned control of slow potential interhemispheric asymmetry in schizophrenia
Int. J. Psychophysiol.
(1999)
B. Güntekin et al.
Emotional face expressions are differentiated with brain oscillations
Int. J. Psychophysiol.
(2007)

C.S. Herrmann et al.

Mechanism of human attention: event-related potentials and oscillations

Neurosci. Biobehav. Rev.

(2001)

W. Klimesch

EEG alpha and theta oscilliations reflect cognitive and memory performance: a review and analysis

Brain Res. Rev.

(1999)

W. Klimesch et al.

Event-related desynchronisation in the alpha band and the processing of semantic information

Brain Res. Cogn. Brain Res.

(1997)

L. Michels et al.

EEG alpha distinguishes between cuneal and precuneal activation in working memory

Neuroimage

(2008)

D. Strüber et al.

MEG alpha activity decrease reflects destabilization of multistable percepts

Cog. Brain Res.

(2002)

P.J. Uhlhaas et al.

Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology

Neuron

(2006)

D. Vernon et al.

The effect of training distinct neurofeedback protocols on aspects of cognitive performance

Int. J. Psychophysiol.

(2003)

Cited by (364)

Successful learning of alpha up-regulation through neurofeedback training modulates sustained attention
2024, Neuropsychologia
As a fundamental attention function, sustained attention plays a critical role in general cognitive abilities and is closely linked to EEG alpha oscillations. Neurofeedback training (NFT) of alpha activity on different aspects of attention has been studied previously. However, it remains unclear how NFT with up- or down-regulation directions modulates sustained attention. Here we employed a counterbalanced single-blind sham-controlled crossover design, in which healthy young adults underwent one NFT session of alpha up-regulation, one NFT session of alpha down-regulation, and one sham-control NFT session over the posterior area. The session order was counterbalanced with a 7-day interval between each session. After each NFT session, the participants completed a visual continuous temporal expectancy task (vCTET) to assess their sustained attention performance. The results showed that compared to sham-control NFT, successful learning of alpha up-regulation resulted in increased reaction time at the beginning of the attention task but a slower increase over vCTET blocks. On the other hand, successful learning of alpha down-regulation had no impact on attention performance compared to sham-control NFT. These findings suggest that successful learning of alpha up-regulation through NFT could impair initial attention performance but slow down visual attention deterioration over time, i.e., alpha enhancement by NFT stabilizing visual attention.
Attention-related power and functional connectivity modulation associated with long-term alpha neurofeedback training
2024, Biomedical Signal Processing and Control
Electroencephalography (EEG) based Neurofeedback training (NFT) is a non-invasive, brain-modulation technique that can improve the subject’s attention. In this research, the effectiveness of long-term NFT on the subject’s attention is explored. Real-time frontal lobe alpha band (8–13 Hz) power was used as feedback. The subjects were divided into the NFT group (n = 25) which received training and the control group (n = 25) which did not. All the subjects participated in the attention network task (ANT) at three stages i.e., before, mid, and at the end of NFT sessions. The EEG and behavioral data (Response time (RT) of the ANT task) were recorded for all subjects. The EEG data were pre-processed using a manual artifact removal procedure to avoid event-related information loss. Alpha band modulation can affect other bands such as theta (4–8 Hz) and beta (13–30 Hz) hence event-related functional connectivity (FC) and band power (BP) were analyzed in these bands as well. In event-related analysis, a significant increase (ANOVA & T-Test: P < 0.05) in theta and alpha power and FC within the NFT group was observed after NFT sessions whereas the changes observed within the control group were not significant. The RT of the subjects in the NFT group decreased. The increase in event-related power and connectivity within the theta and alpha band and the decrease in RT in the NFT group indicate the effectiveness of NFT sessions in the enhancement of attention.
Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals
2023, Biomedical Signal Processing and Control
The movement of eyeballs and eye blinks produces ocular artifacts in the electroencephalogram (EEG) signal during recording. It is necessary to filter out these artifacts from the EEG signal for different biomedical applications. The state-of-the-art (SOTA) methods have less denoising performance to filter ocular artifacts from EEG signals. To overcome this, we have proposed a novel filter-bank-based hybrid approach in this paper to eliminate ocular artifacts from EEG signals. The optimal filter-bank based on the dyadic boundary points based empirical wavelet transform (DBPEWT) is introduced for the multiscale decomposition of EEG signal into various sub-band (SB) signals. The Savitzky–Golay-driven total variation (SGDTV) filter is formulated and applied to the contaminated EEG’s low-frequency range sub-band or first sub-band signal to obtain the filtered sub-band signal. The filtered EEG signal is calculated based on the linear combination of the filtered first sub-band signal evaluated from the SGDTV stage with other sub-band signals of the contaminated EEG signal. The filtering performance of the proposed approach is assessed using the mean absolute error (MAE) in the power spectrum (PS) for $θ$ , $α$ , and $β$ -bands of contaminated and filtered EEG signals. The suggested approach is evaluated using the eye-blink, eye-movement-based ocular artifact-contaminated EEG signals from two public databases. Furthermore, the denoising performance of the proposed filter-bank approach is evaluated using the wearable EEG sensor signals from healthy and epilepsy subjects while performing physical activities such as sitting, walking, stairs, and running, respectively. The results reveal that the proposed approach has obtained the average MAE-PS values for ( $θ$ , $α$ , and $β$ ) bands as (0.156, 0.308, and 0.201), and (0.081, 0.089, and 0.088), respectively for the removal of eye-blink and eye-movement artifacts from EEG signals. The suggested approach has obtained the average MAE-PS values for ( $θ$ , $α$ , and $β$ -bands) as ( $4.555 \pm 0.203$ , $0.057 \pm 0.026$ , $0.062 \pm 0.005$ ), and ( $4.085 \pm 0.086$ , $0.068 \pm 0.043$ , $0.048 \pm 0.006$ ) for healthy and epilepsy subjects during running activity to eliminate artifacts from the wearable sensor-based EEG signals. The denoising performance of the proposed approach has been compared with existing methods to remove eye-movement and eye-blink artifacts from EEG signals.
ITACA: An open-source framework for Neurofeedback based on Brain–Computer Interfaces
2023, Computers in Biology and Medicine
Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain–computer interface (BCI) system that analyzes the user’s brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms.
ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted.
Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies.
ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.
Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults
2023, Biological Psychology
Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants’ neuromodulation ability of high alpha (10–12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.
Improving Mental Skills in Precision Sports by Using Neurofeedback Training: A Narrative Review
2024, Sports

View all citing articles on Scopus

View full text

Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance

Abstract

Research Highlights

Introduction

Section snippets

Design

Trainability

Discussion

Conclusions

Brain Res.

Int. J. Psychophysiol.

Behav. Biol.

Neurosci. Lett.

Clin. Neurophysiol.

Biol. Psychol.

Brain Res. Cogn. Brain Res.

Child Adolesc. Psychiatr. Clin. N. Am.

Int. J. Psychophysiol.

Int. J. Psychophysiol.

Neurosci. Biobehav. Rev.

Brain Res. Rev.

Brain Res. Cogn. Brain Res.

Neuroimage

Cog. Brain Res.

Neuron

Int. J. Psychophysiol.