# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 6 Conclusions and Challenges  
The use of deep neural networks is widespread, while the application of frontier neural networks in EEG-based cognitive workload classification has been delayed for a certain period. One main factor which limits the use of neural networks in EEG-based classification is the lack of experimental data. In this study, we introduce the traditional methods used to dealing with EEG data, the recent works by [PERSON] in the workload classification area and use his public dataset recording 2670 trails of experimental data. Compared to other public datasets only recording trails no more than a few hundred, the dataset can meet the training needs of neural networks. In this study, the method of using spectral maps is improved and a parallel mechanism of spectral feature-enhanced maps is proposed addressing the problems with compressed structural information.  
The final results demonstrate the effects of adding spectral feature-enhanced maps into a network depends on individuals. In the test of generalization among 13 subjects, the maximum increase ratio is 2.7%. The final accuracy test has a mean value of 93.71% with a standard deviation of 0.0027.  
Actually, the monitoring of cognitive load is still in the laboratory stage. This is partly because EEG raw signals are extremely large and when using mobile EEG facilities there is usually a delay. Some breakthroughs in wireless sensor networks, cloud computing and mobile edge computing (MEC) can manage these problems. [PERSON] et al. built a generalization of quality-of-experience (QoE) design in the cloud computing area and eventually fulfilled the service requirements by compromising between delay, service cost and information revealed to the TTP [40]. [PERSON] et al. proposed a GreeDi-based reactive routing protocol aimed at selecting the most efficient route in terms of energy consumption between two nodes in VANETs [41]. [PERSON] et al. built a hierarchical trust-based WSN monitoring model for the smart grid equipment in order to detect Black-Hole (B-H) attacks and the proposed Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS) for wirelessly connected sensor performs at 98.9% detection rate and approximately 99.80% overall accuracy to detect known and unknown malicious behavior in a sensor network [42; 43]. [PERSON] et al. proposed an effective mobile content delivery solution for network delivery and increased content availability without compromising network overhead, regardless of traffic conditions and road networks [44]. [PERSON] et al. introduced a fog-to-fog (F2F) data caching and selection method, which allows IoT devices to retrieve data in a faster and more efficient way. Their presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes [45]. As the edge computing paradigm began to take precedence, a mobile device cloud (MDC) formed at the edge based on idle intra-device resources emerged [46; 47]. All these novel breakthroughs provide technical support for a wider usage of EEG-based monitoring, which is our future work.  
[PERSON] and [PERSON] conceived the main idea, designed the main algorithm and wrote the manuscript. [PERSON] designed the main experiments under the supervision of [PERSON], and the experimental results were analyzed by [PERSON].  
This research was funded by [the Fundamental Research Funding for the Central Universities of Ministry of Education of China] grant number [18D110408] and [the Special Project Funding for the Shanghai Municipal Commission of Economy and Information Civil-Military Inosculation Project \"Big Data Management System of UAVs\"] grant number [JMRH -2018-1042]. And the APC was funded by [the Fundamental Research Funding for the Central Universities of Ministry of Education of China].  
The authors declare no conflict of interest.