# Effects of transcranial alternating current stimulation (tACS) on neural oscillations in speech perception are replicable and predictable

Florian H Kasten, Zoé Darrasse, Quentin Busson, Jules Erkens, Mathieu Marx, Benedikt Zoefel

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## ► To cite this version:

Florian H Kasten, Zoé Darrasse, Quentin Busson, Jules Erkens, Mathieu Marx, et al.. Effects of transcranial alternating current stimulation (tACS) on neural oscillations in speech perception are replicable and predictable. Brain Stimulation, 2025, 18 (5), pp.1617-1619. 10.1016/j.brs.2025.08.016. hal-05232723

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# Effects of transcranial alternating current stimulation (tACS) on neural oscillations in speech perception are replicable and predictable

Florian H. Kasten¹,², Zoé Darrasse¹, Quentin Busson¹, Jules Erkens¹, Mathieu Marx¹,³ & Benedikt Zoefel¹,∗

Dear Editor,

Recent years have seen an increasing number of studies that applied tACS to modulate human speech perception. A common paradigm is to present speech so that its envelope becomes aligned with specific phases of tACS [1,2]. An important role of neural oscillations for speech processing has been demonstrated [3]. If tACS entrains neural oscillations as often assumed [4], then the phase of tACS should, as the oscillation's proxy, therefore also determine speech processing. Indeed, such a result has been reported previously [1,2].

However, the field currently struggles with several issues. First, many effects can be explained without the involvement of neural oscillations. During peaks and troughs of tACS, the electric field applied reaches its extrema, whereas at the zero crossings, the electric field is zero. If the amount of current injected per se influences perception (e.g., by interference or amplification of neural activity), then a regular change in the electric field can lead to an apparent oscillation in perception, without necessarily involving endogenous oscillations. Second, the "best" phase of tACS (that leads to highest accuracy in perceptual measures) is highly variable across subjects [5]. In practice, this can make it difficult to use tACS for behavioural improvements (e.g. in clinical settings), as best phases need to be estimated individually, possibly requiring lengthy procedures prior to the actual intervention.

A recent study [6] has provided solutions to both issues. First, it showed that effects of tACS on speech perception briefly outlast the stimulation. After the offset of 3-Hz tACS, word report accuracy continued to fluctuate rhythmically at the frequency of preceding tACS. Such an "entrainment echo" is important to demonstrate the involvement of neural oscillations which should outlast the rhythmic stimulation once entrained [7], whereas alternative explanations can be ruled out (as no current is injected during the rhythmic behavioural response). Second, the study showed that individual best tACS phases can be predicted from electroencephalography (EEG) responses to rhythmic speech, measured in the same participants but in a different experimental session. The individual phase lag between fronto-central EEG sensors and rhythmic speech correlated with the best tACS phase. This finding suggests that, once the EEG-speech lag is known, best tACS phases can be predicted without the need of sampling all possible tACS phases.

Although both issues seemed to have found their solutions, they relied on a single study [6]. It remained unknown how long the tACS echo in speech perception lasts and whether it is replicable. Likewise, the correlation between EEG and tACS phases was found in exploratory work, and a confirmatory study seemed necessary.

We here report a replication of both results. In a 5-min session, 20 healthy human participants listened to rhythmic speech presented at 3 Hz while their EEG was acquired. From these recordings, we determined participants' individual phase lag between EEG and speech. In their study, van Bree et al [6] identified a common lag (shared between participants) between this individual EEG-speech lag and individual best tACS phase. We used this common lag to predict participants' individual best and worst (180° away) tACS phase for speech perception. In the original report [6], polarity of the stimulation electrodes (i.e. which electrode was connected to which contact of the tACS device) is not clearly defined, and a reversal of the two in the current study (as compared to the original setup) would also lead to a reversal of phase (i.e. best phases become worst and vice versa). However, even in such a case the two phases remain extrema and produce differential effects on speech perception (one enhancing, the other suppressive). For simplicity, we therefore continue to use the labels "best" and "worst" for the two phases targeted, even if an opposite effect on speech perception is possible.
We applied 3-Hz tACS (~1 mA zero-to-peak) over auditory cortical regions (Fig. 1a) while the same participants were asked to report target words embedded in speech-shaped noise. In each trial, tACS was applied for 5 s and then turned off. After tACS offset, the target word was presented at the individually predicted best or worst phase of the preceding tACS (Fig. 1b). We expected a difference in word report accuracy between best and worst tACS phases. To test how long the hypothesized effect lasts, the target word was presented in the first, second, fourth, or eight cycle of the preceding tACS. Accuracy in the word report task was quantified using Levenshtein distance [8], which is the minimum number of edits (deletions, insertions etc.) necessary to change response into target word.

To assess effects of tACS phase on speech perception, we applied a repeated-measures ANOVA with factors PHASE (best vs. worst) and CYCLE (1st, 2nd, 4th, 8th cycle) on participants' word report accuracy (Fig. 1c). We found a statistically reliable interaction between PHASE and CYCLE ($F_{3,57} = 3.32, p = .026, η^2 = .024$), but no main effects of PHASE ($F_{1,19} = 0.17, p = .68, η^2 < .001$) or CYCLE ($F_{3,57} = 1.24, p = .3, η^2 = .017$). This interaction was driven by a difference between best and worst phase only during the first cycle ($t_{19} = 2.27, p = .035, d = .51$) (Fig. 1d). We therefore replicate the finding that phasic effects of tACS on speech perception persist after the stimulation and can be predicted from EEG data [6]. This effect only lasts one cycle, presumably because the entrained oscillation reverts to its natural state.

Participants who synchronised their EEG more strongly to speech (quantified as inter-trial coherence) prior to tACS showed a weaker tACS “echo” in speech perception (smaller performance difference between best and worst phases), but only in the first cycle post-tACS (Fig. 1e). This result indicates that entrained oscillations are easier to manipulate with tACS if their entrainment to speech is weaker, possibly because the two types of entrainment (sound vs. electric stimulation) compete with each other. We provide additional methodological details and a short discussion as Supplementary Information.

Together, we found that tACS effects on neural oscillations in speech perception are replicable and predictable, an important conclusion for an emerging field of brain stimulation.

## Acknowledgements

This work was supported by the Fondation pour l'Audition (grant number FPA-RD-2021-10 to B.Z.) and the Agence Nationale de la Recherche (ANR-21-CE37-0002 to B.Z.).

## Declaration of interests

The authors declare no competing interests.

## References

[1] Riecke L, Formisano E, Sorger B, Başkent D, Gaudrain E. Neural Entrainment to Speech Modulates Speech Intelligibility. Curr Biol 2018;28:161-169.e5.
https://doi.org/10.1016/j.cub.2017.11.033.

[2] Zoefel B, Archer-Boyd A, Davis MH. Phase Entrainment of Brain Oscillations Causally Modulates Neural Responses to Intelligible Speech. Current Biology 2018;28:401-408.e5.
https://doi.org/10.1016/j.cub.2017.11.071.

[3] Poeppel D, Assaneo MF. Speech rhythms and their neural foundations. Nat Rev Neurosci 2020;21:322-34. https://doi.org/10.1038/s41583-020-0304-4.
[4] Kasten FH, Herrmann CS. Discrete sampling in perception via neuronal oscillations—Evidence from rhythmic, non-invasive brain stimulation. European Journal of Neuroscience 2022;55:3402–17. https://doi.org/10.1111/ejn.15006.

[5] Riecke L, Formisano E, Herrmann CS, Sack AT. 4-Hz Transcranial Alternating Current Stimulation Phase Modulates Hearing. Brain Stimul 2015;8:777–83. https://doi.org/10.1016/j.brs.2015.04.004.

[6] van Bree S, Sohoglu E, Davis MH, Zoefel B. Sustained neural rhythms reveal endogenous oscillations supporting speech perception. PLOS Biology 2021;19:e3001142. https://doi.org/10.1371/journal.pbio.3001142.

[7] Pikovsky A, Rosenblum M, Kurths J. Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge: Cambridge University Press; 2001. https://doi.org/10.1017/CBO9780511755743.

[8] Levenshtein VI. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 1966;10:707.

## Figures

<figure><img src="image_3.png" /><figcaption>**Figure 1.** a. tACS electrode montage to stimulate auditory brain regions. b. In each trial, 3-Hz tACS was applied for 5 s and then turned off. A target word was presented in noise at various delays after tACS offset, covering several 3 Hz-cycles. These delays were predicted “best” and “worst” phases for speech perception of the preceding tACS phases, predicted individually from EEG data recorded previously. c,d. Word report accuracy (Levenshtein distance) for best and worst tACS phases (c) and their difference (d) for each of the cycles tested. The tACS “echo” in speech perception is visible as a difference in word report accuracy between best and worst phase in the first cycle after tACS. Note that overall accuracy in the first cycle is not different from that in later cycles, speaking against a mere effect of time (accuracy</figcaption></figure>
changes after tACS offset) and for a genuine effect of phase. e. Correlation between the degree of EEG-
speech synchronisation (as ITC) prior to tACS and the behavioural effect shown in d.

Affiliations

¹ Université de Toulouse, CNRS, Centre de Recherche Cerveau et Cognition (CerCo), 31100 Toulouse, France

² Department for Cognitive, Affective, Behavioral Neuroscience with Focus Neurostimulation, Institute of Psychology, University of Trier, Trier, Germany

³ Service ORL, CHU Purpan, Place du Docteur Baylac, 31059 Toulouse, France

* [benedikt.zoefel@cnrs.fr](mailto:benedikt.zoefel@cnrs.fr)
# Supplementary Information: Effects of transcranial alternating current stimulation (tACS) on neural oscillations in speech perception are replicable and predictable

Florian H. Kasten, Zoé Darrasse, Quentin Busson, Jules Erkens, Mathieu Marx & Benedikt Zoefel

# 1 Supplementary Methods

## 1.1 Participants

21 healthy volunteers joined the experiment and received monetary compensation for participation. They all received written informed consent and were screened for contraindications for electrical stimulation, such as metal implants in the head, head surgery or history of epilepsy by a trained physician (MM). One experiment had to be aborted due to a technical issue that led to corrupted and incomplete data. The final sample consisted of 20 participants (14 females, 23 ± 2.5 years).

## 1.2 Stimuli

Stimuli were constructed from a pool of 474 monosyllabic French words recorded to a metronome at 2 Hz and spoken by a male, native French speaker. The metronome beat was inaudible to participants but ensured that words were perceptually rhythmic if played consecutively, due to their equidistant "perceptual centers (p-centers)" [1]. P-centers are defined as the part of the word that the speaker aligns with a (metronome) beat if asked to speak rhythmically. A speech stimulus is therefore perceived as rhythmic if its p-centers are equally spaced in time. P-centers are defined on a perceptual, not acoustic level. Indeed, it is difficult to reveal an acoustic correlate of the p-center, as speech with equidistant p-centers is not acoustically isochronous [1,2]. It has been speculated that rhythmic speech is produced by aligning linguistic rather than acoustic information to the beat [3]. The alignment of p-centers with the metronome beat in our paradigm ensured that perceptually relevant speech features had a clearly defined phase when presented relative to tACS applied at the same rate (see below). This procedure has led to a successful manipulation of speech perception by tACS in past work [4-6].

Words were time-compressed to 3 Hz using the pitch-synchronous overlap and add (PSOLA) algorithm in the Praat software package, and degraded (while keeping intelligibility) using 16-channel noise-vocoding. Speech stimuli were embedded in speech-shaped noise computed from the average spectrum of all stimuli. Speech stimuli were presented with an SNR of -1 dB. The approach follows our previous work in van Bree et al. [4]. The added noise reduced target word intelligibility to a near-threshold level and ensured a smoother on and offset of the words. Although item-specific factors (e.g., word frequency, phoneme and syllable frequency, phoneme-to-phoneme transition probabilities) can influence word report accuracy, the large pool of words and their random assignment to specific tACS phases (see below) ruled out a systematic relationship between such factors and tACS phase.

Final stimuli were generated in MATLAB 2019a and streamed to a Fireface UCX soundcard (RME Audio, Heimhausen, Germany). Stimuli were presented to participants using an In-Ear headphone system (Etymotic ER-2, Etymotic Research Inc., USA).

## 1.3 Experimental Design

*Estimation of individual tACS phases for target presentation*
Before the main experiment, a 5-min EEG recording was acquired while participants listened to the monosyllabic French words that were presented in random order and continuously at a 3-Hz rhythm. They were asked to indicate the detection of rhythmic deviants via a button press on a standard computer keyboard. They were instructed to keep their eyes open and to fixate on a white cross at the center of a computer screen. From these recordings, we determined participants' individual lag between the 3-Hz phase of the EEG signal at electrode Fz and the stimulus rhythm. Based on the common lag between this EEG-speech lag and best tACS phases identified in van Bree et al. [4], we predicted best and worst tACS phases for individual participants (see their Fig. 6) and used them for target presentation in the subsequent main experiment.

## Main experiment

In each trial, a single target word was randomly selected and presented so that its p-center (the part of the word aligned with the metronome beat) fell at participants' estimated best or worst tACS phase, during the first, second, fourth or eight cycle after tACS offset. Four seconds after tACS offset, participants were asked to type the word they heard on a standard French keyboard. After each experimental block, sensations were monitored via a short questionnaire that participants filled. Each participant performed a total 480 trials of the task. We did not use catch trials, as accuracy would be close to 0 if participants randomly guessed words (low risk of false alarms).

## 1.4 tACS

tACS was applied with two battery-driven stimulators (DC-Stimulator MR, Neuroconn GmbH, Ilmenau, Germany), one for each hemisphere. The tACS electrode montage consisted of a bilateral ring setup attached over auditory regions that produced reliable modulation of speech perception and neural oscillatory activity in previous works [4,5]. Centre electrodes with a diameter of 20 mm were attached at positions T7 and T8 of the conventional EEG 10-10 system. The outer ring electrodes, with a band diameter of 13 mm, were placed around these centre electrodes, with 25 mm distance on all sides. A quarter circle was cut out of the outer rings as to not overlap with the ear. Electrodes were attached with adhesive, conductive ten20 paste (Weaver and Company, Aurora, CO, USA). The electrode montage was prepared in such a way that the skin impedances on both sides of the head were comparable and never exceeded 10 kOhm. To assure synchronization between current and sound, stimulators and participants' earphones were driven by the output of the same sound card (Fireface UCX, RME). Before the start of the experiment, participants were familiarized with the stimulation. Stimulation intensity was individually adjusted to a comfortable level. On average, participants received 1 mA (± 0.19 mA; zero-to-peak) during the experiment.

## 1.5 EEG

The EEG was recorded from 44 active electrodes according to the extended international 10-10 system, using a BioSemi Active 2 amplifier (BioSemi, Amsterdam, Netherlands). Electrodes in the proximity of the tACS electrodes were omitted. EEG signals were recorded at a rate of 2048 Hz and digitally stored on a hard drive using ActiView v9.02 Software (BioSemi, Amsterdam, Netherlands). Electrode offsets were kept below 50 µV.

EEG analyses were performed in MATLAB 2019b using the fieldtrip toolbox [7]. Data was re-referenced to a common average reference and filtered between 1 Hz and 40 Hz using a 4-th order, zero-phase Butterworth filter. An independent component analysis was performed to project out artefacts related to eye-blinks, movements, heart-beat or muscular activity. For each participant separately, the phase lag between the EEG signal at 3 Hz and rhythmic speech, as well as the consistency of these phases across trials (as inter-trial coherence, ITC), were extracted as described in detail in van Bree et al. [4].
## 1.6 Analysis of word report accuracy

Target words and participants' responses were concerted to their phonetic representation using the phonemizer package for Python [8]. The phonemizer performs an automatic text to phoneme conversion using the notation of the international phonetic alphabet. Transformations were screened for errors (i.e., responses such as non-words that were not adequately handled by the algorithm) and manually corrected. The Levenshtein distance (LVDist) between target words and participants' responses was then computed for each trial. The LVDist is the number of reversals, deletions and insertions necessary to transform one string into another [9]. To ensure sufficient sensitivity in the measurement of speech perception, we removed trials with target words with less than three phonemes, and those with entirely correct or incorrect response (LVDist = 0 or 1). In the latter trials, speech perception was either clearly below (LVDist = 1) or above (LVDist = 0), perception threshold, reducing the effect tACS can have (which should have its strongest impact on near-threshold perception).

We used a linear mixed model approach to regress out a potential influence of the length of the target word (number of phonemes) and of sensations produced by tACS from LVDist scores. Participants' scores were modelled as a function of the number of phonemes (random slope across participants) and sensation ratings (random slope across delay conditions). The latter ensured that the model captures potentially different effects of sensations on target words presented with longer temporal delays relative to tACS offset. The residuals of the model were used as a corrected LVDist score for further analysis.

$LVDist \sim N(phonemes + Sensation + (Sensation|Delay) + (N(phonemes|Subj))$

## 2 Supplementary Discussion

Although the presence of “entrainment echoes” in speech perception after tACS supports the involvement of endogenous brain oscillations [4,10], our paradigm cannot distinguish bottom-up and top-down mechanisms. It is possible that tACS entrains brain oscillations which then reverberate passively for some time after tACS offset (see Discussion in [4]). It is also possible that brain oscillations are under top-down control, and this control allows them to subside quickly when the stimulus has stopped. Finally, oscillatory entrainment itself might include some top-down mechanism [11]. For instance, entrainment echoes can be stronger after intelligible speech [4] and have been associated with temporal predictions [12]. Follow-up work contrasting semantically meaningful words with pseudowords or similar control conditions during tACS-induced entrainment echoes will be meaningful in this respect.
References

[1] Scott SK. The point of P-centres. Psychological Research Psychologische Forschung 1998;61:4–11. https://doi.org/10.1007/PL00008162.

[2] Morton J, Marcus S, Frankish C. Perceptual centers (P-centers). Psychological Review 1976;83:405–8. https://doi.org/10.1037/0033-295X.83.5.405.

[3] Fowler CA, Whalen DH, Cooper AM. Perceived timing is produced timing: A reply to Howell. Perception & Psychophysics 1988;43:94–8. https://doi.org/10.3758/BF03208979.

[4] van Bree S, Sohoglu E, Davis MH, Zoefel B. Sustained neural rhythms reveal endogenous oscillations supporting speech perception. PLOS Biology 2021;19:e3001142. https://doi.org/10.1371/journal.pbio.3001142.

[5] Zoefel B, Allard I, Anil M, Davis MH. Perception of Rhythmic Speech Is Modulated by Focal Bilateral Transcranial Alternating Current Stimulation. J Cogn Neurosci 2020;32:226–40. https://doi.org/10.1162/jocn_a_01490.

[6] Zoefel B, Archer-Boyd A, Davis MH. Phase Entrainment of Brain Oscillations Causally Modulates Neural Responses to Intelligible Speech. Current Biology 2018;28:401-408.e5. https://doi.org/10.1016/j.cub.2017.11.071.

[7] Oostenveld R, Fries P, Maris E, Schoffelen J-M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience 2010;2011:e156869. https://doi.org/10.1155/2011/156869.

[8] Bernard M, Titeux H. Phonemizer: Text to Phones Transcription for Multiple Languages in Python. Journal of Open Source Software 2021;6:3958. https://doi.org/10.21105/joss.03958.

[9] Levenshtein VI. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 1966;10:707.

[10] van Bree S, Alamia A, Zoefel B. Oscillation or not-Why we can and need to know (commentary on Doelling and Assaneo, 2021). Eur J Neurosci 2022;55:201–4. https://doi.org/10.1111/ejn.15542.

[11] Barczak A, O'Connell MN, McGinnis T, Ross D, Mowery T, Falchier A, et al. Top-down, contextual entrainment of neuronal oscillations in the auditory thalamocortical circuit. Proceedings of the National Academy of Sciences 2018;115:E7605–14. https://doi.org/10.1073/pnas.1714684115.

[12] Zoefel B, Abbasi O, Gross J, Kotz SA. Entrainment echoes in the cerebellum. Proceedings of the National Academy of Sciences 2024;121:e2411167121. https://doi.org/10.1073/pnas.2411167121.