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A portable solution for simultaneous human movement and mobile EEG acquisition: readiness potential for basketball free-throw shooting
[Submitted on 9 Jan 2025 (v1), last revised 20 Jul 2026 (this ve · 2025-01-10 · via cs.NE updates on arXiv.org

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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.

Submission history

From: Miguel Angel Contreras Altamirano [view email]
[v1] Thu, 9 Jan 2025 17:00:53 UTC (1,173 KB)
[v2] Fri, 10 Jan 2025 10:51:26 UTC (1,173 KB)
[v3] Mon, 20 Jul 2026 16:22:15 UTC (1,914 KB)

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