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<p>Current approaches to investigate the role of neural oscillations in natural scene processing have been limited to artificial stimuli and long data collection. We present a new way to decode real-world scenes participants are viewing from the steady-state visually evoked potentials (SSVEPs) evoked while wearing flickering LCD glasses. We discovered that SSVEP responses from real world scenes are surprisingly complex and have distinct waveform shapes: they differ markedly across scenes and participants but are consistent within individuals, even across multiple days. SSVEP shape varies greatly between stimuli, but is reliable, meaning that decoding works even with a single electrode. Decoding is highly accurate with 5–10&#xa0;s of data and was still above chance level with less than a second of data. Decomposing the SSVEPs into frequency bands showed that the information about the visual scene is present across all of the harmonics of the flicker frequency: optimal decoding used the broadband signals, but with 40&#xa0;Hz (gamma band) showing the highest amount of information after band-pass filters. These findings implicate a broad range of oscillations in encoding real-world scenes, with a particular importance for 40&#xa0;Hz. The SSVEP’s temporal profile is a rich source of information for decoding.</p>

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Decoding real-world visual scenes from alpha and gamma band flicker evoked oscillations in human EEG

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • James Dowsett,
  • Inés Martín Muñoz,
  • Paul Taylor

摘要

Current approaches to investigate the role of neural oscillations in natural scene processing have been limited to artificial stimuli and long data collection. We present a new way to decode real-world scenes participants are viewing from the steady-state visually evoked potentials (SSVEPs) evoked while wearing flickering LCD glasses. We discovered that SSVEP responses from real world scenes are surprisingly complex and have distinct waveform shapes: they differ markedly across scenes and participants but are consistent within individuals, even across multiple days. SSVEP shape varies greatly between stimuli, but is reliable, meaning that decoding works even with a single electrode. Decoding is highly accurate with 5–10 s of data and was still above chance level with less than a second of data. Decomposing the SSVEPs into frequency bands showed that the information about the visual scene is present across all of the harmonics of the flicker frequency: optimal decoding used the broadband signals, but with 40 Hz (gamma band) showing the highest amount of information after band-pass filters. These findings implicate a broad range of oscillations in encoding real-world scenes, with a particular importance for 40 Hz. The SSVEP’s temporal profile is a rich source of information for decoding.