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Although the connection between the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) has been found to be essential for the comprehension of relative clause (RC) sentences, it remains unclear how the LIFG and the LSTG interact with each other, especially during the processing of Chinese RC sentences with different processing difficulty. This study thus conducted a 2 × 2 (modifying position × extraction position) factorial analyses to examine how these two factors influences regional brain activation. The results showed that, regardless of the modifying position, greater activation in the LIFG was consistently elicited in Chinese subject-extracted relative clauses (SRCs) with non-canonical word order than object-extracted relative clauses (ORCs) with canonical word order, implying that the LIFG subserving the ordering process primarily contributes to the processing of information with increased integration demands due to the non-canonical sequence. Moreover, the directional connection between the LIFG and the LSTG appeared to be modulated by different modifying positions. When the RC was at the subject-modifying position, the effective connectivity from the LIFG to the LSTG was dominantly activated for sentence comprehension; whereas when the RC was at the object-modifying position thus being more difficult, it might be the feedback mechanism from the LSTG back to the LIFG that took place in sentence processing. These findings reveal that brain activation in between the LIFG and the LSTG may be dynamically modulated by different processing difficulty and suggest the relative specialization but extensive collaboration involved in the LIFG and the LSTG for sentence comprehension.

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.

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