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REVIEW 2 major objections 5 minor 26 references

A portable solution for simultaneous human movement and mobile EEG acquisition: readiness potential for basketball free-throw shooting

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Two off-the-shelf smartphones plus a wireless EEG cap can record the readiness potential during real basketball free throws.

desk verdict A credible mobile-EEG feasibility study with a load-bearing but fixable movement-onset validation gap. read the letter →

arxiv 2501.05378 v3 pith:6622CDZ5 submitted 2025-01-09 cs.NE q-bio.NC

Miguel Contreras-Altamirano , Melanie Klapprott , Nadine Jacobsen , Paul Maanen , Julius Welzel , Stefan Debener This is my paper · ORCID
classification cs.NEq-bio.NC
keywords mobileEEGreadinesspotentialbasketballfreethrowhumanposeestimationMediaPipebrain/bodyimaginginertialmeasurementunitecologicalvalidity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Twenty-six experienced basketball players shot 120 free throws each while wearing a 32-channel wireless EEG cap and a wrist-worn motion sensor, with two smartphones recording brain signals and full-body pose. The paper's central claim is that this pocketable, low-cost rig is enough to capture the neural preparation for a skilled whole-body movement in a real court setting. The main supporting result is a clear readiness potential, a slow negative voltage shift over fronto-central scalp, starting around 400 ms before the detected movement onset, with the strongest effect at electrode Cz. The paper also reports that readiness-potential amplitude did not distinguish successful from unsuccessful shots, while pose analysis showed participant-specific movement differences between hits and misses in 10 of 26 players. If correct, this establishes that ecologically valid, out-of-the-lab studies of brain-body coupling are feasible with consumer hardware.

What carries the argument

The load-bearing tool is the synchronized recording chain. One smartphone receives 32-channel EEG at 250 Hz from a head-mounted wireless amplifier along with the amplifier's inertial sensors; a second smartphone runs MediaPipe Pose Landmark Detection at 15 Hz to track 33 body landmarks; and a wrist-worn IMU streams acceleration at 60 Hz. Lab Streaming Layer binds the streams into a single file. To time-lock brain activity, the analysis first detects a set-point when the right wrist crosses eye level on the pose stream, then walks backward through the wrist acceleration signal to find movement onset as the last sample below a threshold of baseline mean plus one standard deviation. EEG epochs from -2.5 s to movement onset are cleaned with ICA-based artifact removal and averaged to produce the readiness potential. The synchronization of EEG, pose, and IMU timestamps to that movement onset is what makes a comparison between neural preparation and subsequent movement outcome possible.

What would settle it

Take the same two-phone recordings and redefine movement onset using a high-speed camera or the first detectable motion of the right throwing hand rather than the wrist-accelerometer threshold; if the significant negative deflection at Cz disappears or shifts outside the -400 to 0 ms window, the claim that the portable setup captures the readiness potential is not established.

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Extended reading notes

Core claim

The discovery this paper argues for is that a minimally invasive, low-cost two-smartphone system can simultaneously capture human pose and EEG well enough to observe a canonical brain signature of action preparation in a natural skilled task. Averaged across participants, the EEG shows a readiness potential over fronto-central channels, with a significant negative deflection at Cz from -400 to 0 ms before movement onset (mean -6.54 to -13.52 µV; z = -2.53 to -3.92; FDR-corrected p = 0.049 to 0.003; r = 0.50 to 0.77). However, the amplitude of the RP was not related to shooting outcome at the group or single-trial level (largest mean R² = 0.047, i.e., 4.7% explained variance), and only a minority of participants showed pose differences between successful and unsuccessful attempts. The authors present this as evidence that the setup is sufficient for monitoring movement and brain dynamics outside the laboratory, not that the RP forecasts performance.

Load-bearing premise

The load-bearing premise is that the wrist-worn accelerometer's threshold, baseline mean plus one standard deviation, marks the true onset of the shot, because EEG epochs, pose epochs, and every performance correlation are aligned to that time zero; the paper's own validation found the most consistent motion at that instant in the left hand and hips, not the right throwing hand.

Editorial extensions

If this is right

  • Readiness potentials can be elicited and recorded during whole-body, goal-directed motor skills outside the laboratory, extending the RP beyond finger presses and isolated movements.
  • RP amplitude before a free throw does not predict make versus miss in this population; performance is better linked, for some individuals, to body posture during execution.
  • A two-smartphone rig with a wireless EEG cap is enough to align neural, inertial, and pose data streams in a natural setting, reducing cost and mobility barriers.
  • Pose-based set-point detection plus reverse thresholding provides a practical event marker for natural movements that lack a discrete button-press onset.
  • This supports further mobile brain-body imaging studies and possibly portable neurofeedback applications in real environments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The null RP-performance relationship may be a timing artifact of the onset marker: if the wrist IMU threshold captures a preparatory crouch or ball hand-over rather than throw initiation, single-trial RP estimates would be systematically jittered, biasing point-biserial correlations toward zero; re-epoching around a video-verified release frame would test this.
  • The pose differences, lower wrist before the shot, higher wrist at set-point, and stable head, suggest a compact marker set for automated coaching feedback, but the small explained variance and individual specificity mean such feedback would need to be personalized rather than generic.
  • Because the setup is cheap and untethered, a natural extension would be to run the same rig with elite versus novice shooters; expertise differences in RP amplitude found in earlier work might reappear in between-subject designs even though within-subject trial outcome did not correlate.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper reports a portable, low-cost mobile brain/body imaging setup consisting of two Android smartphones, a wireless 32-channel EEG amplifier, and a wrist-worn IMU, used to record brain activity and full-body pose while 26 basketball players performed 120 free throws each. The authors analyze (i) the readiness potential (RP) preceding the throwing movement, (ii) whether RP amplitude differs between successful and unsuccessful shots, and (iii) whether pose landmarks differ between hits and misses. They report a significant negative deflection at Cz and adjacent fronto-central channels from -400 to 0 ms before their accelerometer-derived movement onset, no significant relationship between RP amplitude and shooting success, and exploratory pose differences in 10 of 26 participants with small explained variance. The paper concludes that the setup is sufficient to monitor complex human movement and associated brain dynamics outside the laboratory.

Significance. If the movement-onset alignment is valid, this is a useful methodological contribution to mobile EEG and MoBI research. The setup is genuinely portable, low-cost, and lightweight; the authors provide open code and data availability; and the statistical reporting is largely transparent, including FDR corrections, effect sizes, and an honest presentation of null results for the RP-performance relationship. The pose analysis is explicitly exploratory, and the small effect sizes are not overstated. The central risk is the validity of the accelerometer-derived movement onset, which the paper's own validation does not confirm. Because every EEG epoch, pose window, set-point latency, and performance correlation is time-locked to that event, this issue is load-bearing for the main feasibility claim. The paper would be strengthened substantially by independent validation of the onset marker, for example via video annotation, EMG, or a second motion sensor on the ball.

major comments (2)
  1. [Results, 'Movement onset validation'; Figure 3] The validation of the movement-onset marker does not confirm that the right-wrist accelerometer threshold identifies the onset of the throwing movement. The binomial tests across participants show significant motion at the accelerometer-defined t=0 in the left index finger, left pinky, left thumb, left wrist, and both hips, but not in the right throwing wrist. Since the right-wrist IMU defines t=0 for all subsequent analyses, this result suggests that the threshold may be capturing a preparatory postural adjustment, a ball-handover movement, or a cross-device synchronization artifact rather than the initiation of the free-throw. The RP-like morphology could still arise if the same non-throwing movement consistently precedes each shot by a roughly constant interval, but in that case the paper's central claim of capturing the readiness potential for the basketball free-throw is not established. The authors should validate the onset against an independent ground-truth marker (e.g., manual video annotation, EMG, or a second sensor on the ball) or, at minimum, quantify and discuss the temporal offset between the detected event and the actual throwing onset.
  2. [Materials and Methods, 'Onset detection'; Limitations] The threshold-based onset detection procedure is described only qualitatively. No trial-to-trial or participant-level variability of the detected onsets is reported, and the set-point latency is given only as a group mean (544 ms after onset) without a range or standard deviation. In addition, the Limitations section acknowledges 'minimal delays in synchronization' (citing Iwama et al., 2024) but does not quantify the jitter or latency between the EEG, PLD, and IMU streams, despite the fact that all streams were aligned via linear interpolation on EEG timestamps. Because the entire analytic pipeline depends on the temporal alignment of these streams, the authors should provide quantitative synchronization error estimates and assess the stability of the onset detection across trials and participants.
minor comments (5)
  1. [Materials and Methods, 'Presence of the RP' vs. Results, 'Presence of the RP'] The Methods state that the presence of the RP was evaluated using t-tests, but the Results report Wilcoxon signed-rank tests; please reconcile this discrepancy.
  2. [Abstract and Results, 'Presence of the RP'] The abstract reports effect sizes as r = 0.50 to 0.77, but the Results do not explain how these are derived from the reported z-values; please specify the computation (e.g., r = z / sqrt(N)).
  3. [Results, 'Movement onset validation'] The description of the binomial tests is incomplete: it is not stated whether the test is performed on the proportion of participants showing a significant Wilcoxon effect at each body part, and the correction for multiple comparisons across body parts is not explained in enough detail to be reproduced.
  4. [Discussion, 'Movement onset validation'] The sentence 'Inspection of the PLD signals confirmed that hand/wrist motion was among the first body parts involved in initiating a basketball free-throw motion sequence' appears to contradict the statistical validation reported in the Results, which found the most consistent movement at the left hand and hips; please clarify or qualify this statement.
  5. [References] The reference list contains an entry for 'Hayes, M.H.S. and Patterson, D.G. (1921)' that is not cited anywhere in the text; please remove it or cite it appropriately.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central RP and pose results are empirical measurements time-locked to an accelerometer onset, not outputs defined by inputs; self-citations are limited to software tools and are not load-bearing.

full rationale

This is an empirical measurement and feasibility study without a formal derivation chain. The central claim—that a portable smartphone-and-wireless-EEG setup can capture the readiness potential before basketball free throws—is supported by time-locking EEG to an accelerometer-derived movement onset and testing whether the pre-onset EEG amplitude deviates from zero. The EEG signal is independent of the accelerometer threshold, so the observed negative deflection is not imposed by the onset definition; it is a genuine empirical result, albeit one whose accuracy depends on the validity of the onset marker. The null relationship between RP amplitude and shooting outcome and the exploratory pose differences are descriptive correlations, not predictions from fitted parameters. The paper does cite the authors' own software tools (Blum et al. 2021; Maanen et al., in preparation) for LSL-based synchronization and pose streaming, but these citations support the recording infrastructure rather than the empirical conclusion, and the conclusion does not reduce to them. The main weakness identified in the paper—that the PLD validation found the most consistent motion at movement onset in left-hand and hip landmarks rather than the right throwing wrist—is a measurement-validity concern, not a circularity: the onset definition and the EEG outcome are not equivalent by construction. No equation, fitted parameter, or uniqueness claim is defined in terms of its own output. Therefore, no significant circularity is present.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new theoretical entities or fitted physiological constants. The listed free parameter and axioms are methodological and domain assumptions that any reader should inspect before trusting the trial-aligned averages.

free parameters (1)
  • Movement onset threshold = baseline mean + 1 SD of wrist acceleration, applied per participant
    Chosen by hand and data-derived; it defines time zero for all EEG and pose epochs, so the RP analysis depends on it. It is a standard criterion, not a fitted model parameter for prediction.
assumptions (5)
  • domain assumption Self-reported right-handedness and at least 3 years of regular basketball experience are accurate inclusion criteria (Methods, Participants).
    The study relies on these self-reports to justify the task and the interpretation of the right wrist as the throwing hand.
  • domain assumption The right-wrist IMU acceleration signal, thresholded at baseline mean plus one SD, marks the true onset of the shooting movement (Methods, Onset detection).
    All EEG and pose epochs are aligned to this time zero; the paper's own validation shows unexpected left-hand/hip motion at this point, so the assumption is fragile.
  • domain assumption Linear interpolation of 15 Hz PLD data and 60 Hz IMU data onto 250 Hz EEG timestamps preserves temporal alignment well enough for 100 ms bins (Methods, EEG data analysis).
    The authors acknowledge possible synchronization delays (citing Iwama et al. 2024) and do not quantify residual jitter.
  • domain assumption ICA plus ICLabel reliably separates movement and physiological artifacts from neural EEG in a throwing task (Methods, EEG preprocessing).
    The whole-brain signal interpretation rests on artifact removal being valid in mobile conditions; the authors note residual noise may remain in some participants.
  • domain assumption MediaPipe PLD 2D coordinates (with z discarded) are sufficient to characterize whole-body posture for the free-throw task (Methods, Materials; Discussion).
    The authors themselves note z-axis depth is unreliable and single-camera 2D pose may miss relevant motion.

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Pith. "Pith review of A portable solution for simultaneous human movement and mobile EEG acquisition: readiness potential for basketball free-throw shooting." pith.science (2026). https://pith.science/paper/6622CDZ5

@misc{pith2026250105378,
  author       = {Pith},
  title        = {Pith review of: A portable solution for simultaneous human movement and mobile EEG acquisition: readiness potential for basketball free-throw shooting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6622CDZ5}},
  note         = {Machine review of arXiv:2501.05378}
}
abstract

Advances in wireless electroencephalography (EEG) technology promise to record brain-electrical activity in everyday situations. To better understand the relationship between brain activity and natural behavior, it is necessary to monitor human movement patterns. Here, we present a pocketable setup consisting of two smartphones to simultaneously capture human posture and EEG signals. We asked 26 basketball players to shoot 120 free throws each. First, we investigated whether our setup allows us to capture the readiness potential (RP) that precedes voluntary actions. Second, we investigated whether the RP differs between successful and unsuccessful free-throw attempts. The results confirmed the presence of the RP over fronto-central channels, with significant negative deflection at channel Cz, from -400 to 0 ms before movement onset ($M$ $\pm$ $SE$: -6.54 $\pm$ 2.26 to -13.52 $\pm$ 2.42 $\mu$V; $z$ = -2.53 to -3.92; FDR-corrected $p$ = 0.049 to 0.003; $r$ = 0.50 to 0.77). However, the amplitude of the RP was not related to shooting success (all FDR-corrected $p$ > 0.05; maximum mean $R^2$ = 0.047, i.e., 4.7% explained variance). Preliminary exploratory pose analysis conducted offline indicated the presence of participant-specific variations in posture between successful and unsuccessful shots in 38.5% of participants (10/26), with 4.5% explained variance (maximum mean landmark $R^2$ = 0.045). We conclude that a highly portable, low-cost and lightweight acquisition setup, consisting of two smartphones and a head-mounted wireless EEG amplifier, is sufficient to monitor complex human movement patterns and associated brain dynamics outside the laboratory.

Figures

Figures reproduced from arXiv: 2501.05378 by the authors.

Figure 4
Figure 4. In the grand average RP, the topographic maps provide a clear visual representation of the spatial and temporal distribution of activity, indicating an onset of the RP at approximately -1000 ms prior movement. The spatial distribution is consistent with the expected (fronto-) central localization of pre-movement neural activity. We tested the grand average RP using the Wilcoxon test for each of the 15 time segments … view at source ↗
Figure 1
Figure 1. Pocketable setup for basketball free-throw shooting. Two tripods are used to keep two Android smartphones in fixed positions. One smartphone wirelessly receives EEG data recorded along with video recordings from the same phone (Smarting Pro app). The second smartphone captures human motion in real-time (MediaPipe Pose Landmark Detection app). A single Movella DOT sensor placed at the right wrist streams IMU signals.… view at source ↗
Figure 2
Figure 2. EEG preprocessing pipeline. To obtain the ICA weights, bad channels were rejected, data was filtered, bad epochs were removed, and an extended infomax ICA was run. The ICA weights were merged with the raw data for further preprocessing. At the end the RP was parameterized and submitted to statistical analysis [PITH_FULL_IMAGE:figures/full_fig_p030_2.png] view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Root mean square (RMS) of acceleration magnitude over time [PITH_FULL_IMAGE:figures/full_fig_p031_3.png]
Figure 4
Figure 4. Figure 4: Grand average of joint human motion capture and ERP. [PITH_FULL_IMAGE:figures/full_fig_p032_4.png]
Figure 5
Figure 5. Figure 5: Grand average ERP comparison between conditions across participants [PITH_FULL_IMAGE:figures/full_fig_p033_5.png]
Figure 6
Figure 6. Figure 6: Explained variance (R²) from point￾bi-serial correlation of ERP features between conditions of participants in Fz channel. The heat-map illustrates the R² of ERP features at channel Fz, representing the proportion of variance in trial outcomes (hits versus misses) expl…
Figure 7
Figure 7. Figure 7: Differences in pose landmarks between hits and misses. Point bi-serial correlation analysis identified significant pose differences between hits and misses across participants during specific time windows while basketball shooting. Resulting p-values for the X, Y, and …

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    2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.