Alpha down-regulation neurofeedback training effects on implicit motor learning and consolidation

Abstract

Objective. Implicit motor learning, which is a non-conscious form of learning characterized by motor performance improvement with practice, plays an essential role in various daily activities. Earlier study using neurofeedback training (NFT), a type of brain-computer interaction that enables the user to learn self-regulating his/her own brain activity, demonstrated that down-regulating alpha over primary motor cortex by NFT could immediately facilitate the implicit motor learning in a relatively simple motor task. However, detailed effects on EEG and implicit motor learning due to NFT especially in a more complex motor task are still unclear. Approach. We designed a single-blind sham-controlled between-subject study to examine whether alpha down-regulation NFT could facilitate implicit motor learning and also its consolidation in a more difficult and motor predominant task. At left primary motor cortex (C3) in two days, the alpha NFT group received alpha down-regulation training through auditory feedback while the sham-control group received random beta NFT. At the end of NFT, all participants performed the continuous tracking task with their dominant (right) hand to evaluate the implicit motor learning immediately. Finally, the continuous tracking task was performed again on the next day to assess consolidation effects. Main results. The alpha NFT group successfully decreased alpha amplitude during NFT, whereas the sham-control group maintained alpha at a relatively stable level. There was unfortunately no statistical evidence proving that the alpha NFT group significantly enhanced the implicit motor learning at the end of NFT and the consolidation on the next day compared to the sham-control group. Nevertheless, a significant correlation was found between the alpha change trend during NFT and the implicit motor learning for all participants, suggesting that faster alpha down-regulation was associated with better implicit motor learning. Significance. The findings suggested a close link between implicit motor learning and alpha change induced by NFT.

Effects of Transcranial Alternating Current Stimulation and Neurofeedback on Alpha (EEG) Dynamics: A Review

“…There are also NFB studies reporting positive correlations between the level of behavioral improvement and the magnitude of increased online alpha activity (Nan et al, 2012 , 2013 ; Bobby and Prakash, 2017 ). A positive correlation between the magnitude of behavioral improvement and the magnitude of online alpha-band responsiveness to NFB has been found for working memory (Nan et al, 2012 ; Bobby and Prakash, 2017 ), attention (Berger and Davelaar, 2018 ), and motor learning (Nan et al, 2020 ). To the best of our knowledge, there are no tACS multi-session studies investigating the session-to-session evolution of alpha dynamics during tACS online periods.…”

“…In the alpha-amplitude upregulation protocol, progressive session-to-session increases in alpha amplitude during the NFB sessions were reported in several studies (Zoefel et al, 2011;López-Larraz et al, 2012;Nan et al, 2013;Wan et al, 2014;Bobby and Prakash, 2017;Naas et al, 2019). Progressive increases in the ability to manipulate the alpha-band dynamics relative to the NFB protocol have been also observed in alpha-downregulation protocols (Wan et al, 2016;Nan et al, 2018Nan et al, , 2020. To the best of our knowledge, no study has investigated session-to-session online changes in the alpha-band responsiveness relative to multiple sessions of alpha tACS.…”

“…In other words, all multi-session tACS were focused on the offline alpha activity (Schmidt et al, 2013 ; Clancy et al, 2018 ; Ahn et al, 2019 ; He et al, 2019 ). On the other hand, NFB research, which seems to have a longer history of studying the dynamics of alpha activity, involves a considerable number of multi-session studies focusing on studying the offline alpha (Kerson et al, 2009 ; Breteler et al, 2010 ; Alexeeva et al, 2012 ) as well as the online dynamics of alpha activity (Bobby and Prakash, 2017 ) or both (Plotkin, 1978 ; Dempster and Vernon, 2009 ; Escolano et al, 2011 ; López-Larraz et al, 2012 ; Nan et al, 2013 , 2020 ; Dekker et al, 2014 ; Hsueh et al, 2016 ; Wan et al, 2016 ). With regard to the NFB studies devoted to showing the alpha behavior during both online and offline periods of the NFB session, it is worth mentioning that some studies reported an increase of alpha-band responsiveness relative to NFB modulation only during online periods and with no changes during offline periods (Nan et al, 2012 , 2020 ; Hsueh et al, 2016 ; Wan et al, 2016 ).…”

“…Within tACS research, factors, such as aging (Fresnoza et al, 2020 ), actual health condition (Krause et al, 2014 ), baseline (pre-stimulation) level of the targeted EEG activity (Neuling et al, 2013 ; Alagapan et al, 2016 ; Ruhnau et al, 2016 ; Bächinger et al, 2017 ; Lefebvre et al, 2017 ; Berger et al, 2018 ), placebo (Antal and Herrmann, 2016 ), ceiling effect (Krause et al, 2014 ; Fresnoza et al, 2020 ), a specific type of cognitive activity during which tACS is induced (Feurra et al, 2013 ), and illumination condition (Kanai et al, 2008 , 2010 ; Stecher et al, 2017 ), have been examined. In NFB research, several factors related to brain state have been investigated, including baseline level of the target EEG activity (Travis et al, 1974 ; Wan et al, 2014 ; Nicholson et al, 2016 ; Nan et al, 2020 ), the level of illumination of the treatment room (Paskewitz and Orne, 1973 ; Cram et al, 1977 ), ceiling effect (Hardt and Kamiya, 1978 ), placebo (Mullinix et al, 1978 ; Plotkin and Rice, 1981 ; Holroyd et al, 1984 ; Kotchoubey et al, 2001 ; Thibault and Raz, 2017 ; Shibata et al, 2019 ), aging (Staufenbiel et al, 2014 ), the brain morphology (Enriquez-Geppert et al, 2013 ), healthy brain vs. pathological condition (Ros et al, 2017a ), the effect of fatigue (Choobforoushzadeh et al, 2015 ), the effect of anxiety (Hardt and Kamiya, 1978 ; Kadosh and Staunton, 2019 ), the effect of mental strategies used for enhancing NFB-rewarded neural activity (Lacroix and Roberts, 1978 ; Sepulveda et al, 2016 ; Lubianiker et al, 2019 ; Shibata et al, 2019 ), the effects of mood (Kadosh and Staunton, 2019 ), and the personality traits (Ancoli and Kamiya, 1978 ; Peciuliene et al, 2015 ). Among all these investigated domains, both tACS and NFB have investigated the following four types of factor in relation to the responsiveness of alpha band to neuromodulation: (1) baseline level of targeted EEG activity, (2) illumination condition, (3) placebo effect, and (4) ceiling effect.…”

Alpha activity neuromodulation induced by individual alpha-based neurofeedback learning in ecological context: A double-blind randomized study

“…As brain-computer interfaces (BCI) applications, NF consists in providing real-time, closed-loop-auditory, visual, or tactile-feedback of a subject's brain activity to train self-regulation of specific brain patterns related to a targeted ability. NF applications have been developed since the 70's in non-clinical [1][2][3] and clinical settings, such as epilepsy 4 , attentiondeficit hyperactivity disorder [5][6][7][8][9] , depression 10,11 , psychopathy 12,13 , and anxiety 10,[14][15][16] . However, the understanding of the neurocognitive mechanisms during a BCI task or a NF training (NFT) remains elusive 17,18 .…”