Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography

Abstract

Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60–145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.

An ecological investigation of the capacity to follow simultaneous speech and preferential detection of ones’ own name

“…First reported by Cherry (1953), the own name advantage has been demonstrated in a variety of behavioral and neural studies, arguably reflecting its ‘special status’ in the perceptual system (Deutsch and Deutsch, 1963; Gallagher, 2000; Perrin et al, 2005; Alho and Vorobyev, 2007). The most commonly known, yet also somewhat controversial, finding is of detecting ones’ name in a presumed “unattended” stream, in selective attention paradigm (Wood and Cowan, 1995; Röer et al, 2013; Ljungberg et al, 2014; Naveh-Benjamin et al, 2014; Holtze et al, 2021) although behavioral advantages have been shown in a variety of other paradigms (Perrin et al, 1999; Tamura et al, 2012; Tateuchi et al, 2012; Röer et al, 2013; Lechinger et al, 2016; Liu et al, 2019; Jijomon and Vinod, 2021; Ye et al, 2021). Several ERP components have also been shown to be enhanced in response to hearing or reading ones’ own name vs. other names or words.…”

Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces

“…The processing of the acoustic name stimulus in the human brain involved a distributed connectivity network (Northoff and Bermpohl, 2004 ; Davey et al, 2016 ). For example, the acoustic name stimulus activates not only low-level auditory sense in the primary auditory cortex (mainly in the transverse temporal gyrus and the superior temporal gyrus) (Pickles, 2013 ; Nakai et al, 2017 ), but also activates high-level cognition in other structures such as the medial prefrontal cortex, inferior parietal lobule (Davey et al, 2016 ), the insula (Qin et al, 2012 ; Ye et al, 2021 ), and the fusiform (Carmody and Lewis, 2006 ). Parts of these findings by fMRI and scalp EEG measurements could be verified in the current study, showing that activated brain regions for the acoustic name stimulus distributed broadly in the temporal lobe, parietal lobe, and deeper structures such as the insula and fusiform ( Table 2 ).…”

“…Second, in the current classification scheme, each feature only made a general description for a sliding window instead of several values at different time points within the window, which might not capture subtle differences between the two stimuli. In future work, we would adopt a cascade classification scheme to overcome this problem, where the HMM detects the active state onset first, and then another classifier could distinguish the type of the stimulus using the information at different time points within the active state (Ye et al, 2021 ). Based on a sufficient number of subjects in the future, we would reopen the investigation on statistical significance of comparisons between the four-type feature combination and each single type of informative feature.…”