Impact of mobile phone use on the brain activity: Audio call vs video call.

Abstract

The growing dependence on mobile phones for communication has raised concerns regarding the neurological impact of radio-frequency electromagnetic fields, especially in audio and video calls. This research investigates the impact of WhatsApp audio and video calls on cognitive load and mental fatigue using non-invasive electroencephalography (EEG) signals. EEG signals were recorded from 28 healthy participants during baseline, 3-minute, and 4-minute call sessions, with participants equally distributed between audio and video call groups. The signals were preprocessed using bandpass filtering, continuous wavelet transform, and independent component analysis to isolate theta and alpha frequency bands. Cognitive load was assessed using the theta-alpha ratio (TAR), and mental fatigue was measured using Mahalanobis distance-based analysis of theta and alpha rhythms. The findings reveal that video calls impose significantly more cognitive load (e.g., TAR mean: 0.78 (SD: 0.36) for 3-minute video call and mean: 0.76 (SD: 0.37) for 3-minute audio call, p<0.05) and mental fatigue (e.g., 42.11 microvolts for 4-minute video call and 38.84 microvolts for 4-minute audio call) than audio calls, and both effects become stronger for prolonged durations. Machine learning (ML) classification also demonstrated high separability, with receiver operating characteristic analysis exhibiting area under the curve values above 0.90 for distinguishing call conditions. These results demonstrate that video communication places higher cognitive and fatigue demands as compared to audio communication. By integrating EEG indices, self-report measures, and ML classification, this study exhibits convergent evidence for the cognitive impact of mobile phone calls and illustrates the necessity to consider communication modality when assessing workload and fatigue in real-world contexts.

AI evidence extraction

Main findings

EEG-based indices indicated that video calls produced significantly higher cognitive load than audio calls (example TAR means reported for 3-minute sessions; p<0.05) and higher mental fatigue (example values reported for 4-minute sessions). Effects were stronger with longer call duration, and ML classification distinguished call conditions with ROC AUC > 0.90.

Outcomes measured

• EEG theta-alpha ratio (TAR) as cognitive load
• EEG-based mental fatigue (Mahalanobis distance-based analysis of theta and alpha rhythms)
• Machine learning classification performance (ROC AUC for distinguishing call conditions)
• Self-report measures (mentioned)

Limitations

• RF exposure metrics (e.g., frequency, SAR) not reported in abstract
• Short exposure durations (3–4 minutes)
• Between-group design implied (participants equally distributed between audio and video groups) rather than within-subject crossover (not fully detailed)
• Outcomes are surrogate/physiological measures (EEG indices) rather than clinical endpoints

Suggested hubs

• mobile-phones-rf (0.86)
  Study evaluates brain activity/cognitive load during mobile phone audio vs video calls, framed as RF-EMF concern.

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    "population": "28 healthy participants",
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    "main_findings": "EEG-based indices indicated that video calls produced significantly higher cognitive load than audio calls (example TAR means reported for 3-minute sessions; p<0.05) and higher mental fatigue (example values reported for 4-minute sessions). Effects were stronger with longer call duration, and ML classification distinguished call conditions with ROC AUC > 0.90.",
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        "Outcomes are surrogate/physiological measures (EEG indices) rather than clinical endpoints"
    ],
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    "peer_reviewed_likely": "yes",
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        "theta-alpha ratio",
        "cognitive load",
        "mental fatigue",
        "machine learning",
        "ROC AUC"
    ],
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