EEG phase patterns reflect the representation of semantic categories of objects

Abstract

Oscillations of electroencephalographic signals represent the cognitive processes arose from the behavioral task and sensory representations across the mental state activity. Previous studies have shown the relation between event-related EEG and sensory-cognitive representation and revealed that categorization of presented object can be successfully recognized using recorded EEG signals when subjects view objects. Here, EEG signals in conjunction with a multivariate pattern recognition technique were used for investigating the possibility to identify conceptual representation based on the presentation of 12 semantic categories of objects (5 exemplars per category). Using multivariate stimulus decoding methods, surprisingly, we demonstrate that how objects are discriminated from phase pattern of EEG signals across the time in low-frequency band (1-4 Hz), but not from power of oscillatory brain signals in the same frequency band. In contrast, discrimination accuracy from the power of EEG signals has significantly higher than the performance from phase of EEG signal in the high-frequency band (20-30 Hz). Moreover, our results indicate that how the accuracy of prediction changes between various areas of brain continuously across the time. In particular, we find that, during the object categorization task, the inter-trial phase coherence in low-frequency band is significantly higher than other frequency in various regions of interests. This measure is associated with decoding pattern across the time. These results suggest that the mechanism underlying conceptual representation can be mediated by the phase of oscillatory neural activity.

Characterizing the roles of alpha and theta oscillations in multisensory attention

“…The ITPC therefore provides a measure of the consistency of the oscillations we observed, and may provide insight into the importance of phase in the mechanisms underlying attention. ITPC values that considered relatively high in recent work with human subjects range from approximately 10% to 20% (Behroozi, Daliri, & Shekarchi, 2016; Almeida et al, 2016). $$\text{ITPC}false(f,tfalse)=\frac{1}{n}true\sum _{k=1}^n\frac{F_k(f,t)}{\left|F_{k}false(f,tfalse)\right|}$$…”

A New Method to Detect Event-Related Potentials Based on Pearson’s Correlation

“…The conservation of the original phase of signals is very important for the application of our method. On the other hand, the conservation of the information about the phase pattern of the signals, rather than the simple power of the signals, was found important also in the representation of semantic categories of objects, especially in the low-frequency band (1 to 4 Hz) [4].…”

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

“…These analyses generally rely on the signal 'mean' amplitude, which although informative, but might be a sub-optimal feature to decode the object category information from. The use of this sub-optimal feature might thus hide the true spatiotemporal dynamics of categorical object encoding in the brain, which is still debated in cognitive neuroscience (Majima et al, 2014;Karimi-Rouzbahani et al, 2017a;Behroozi et al 2016). Here, we characterize the most informative features or features of the recorded brain activity, which provide object category information, and evaluate their relevance by measuring how well each feature explains behavioral object recognition performance.…”

“…To address this issue, studies have investigated other features of brain activations such as phase (Behroozi et al, 2016;Watrous et al, 2015;Torabi et al, 2017;Wang et al, 2018), signal power across frequency bands (Rupp et al, 2017;Miyakawa et al, 2018;Behroozi et al, 2016), time-frequency features such as Wavelet coefficients Taghizadeh-Sarabi et al, 2015), inter-electrode correlation (Majima et al, 2014;Karimi-Rouzbahani et al, 2017a), non-linear statistical features (Joshi et al, 2018;Torabi et al, 2017;Stam, 2005). Behroozi et al, (2016) decoded object categories using signal phase patterns in the delta frequency band (i.e. 1-4 Hz).…”