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- The influence of noise in the neurofeedback training sessions in student athletesPublication . Domingos, Christophe; Caldeira, Higino da Silva; Miranda, Marco; Melicio, Fernando; Rosa, Agostinho; Pereira, José GomesConsidering that athletes constantly practice and compete in noisy environments, the aim was to investigate if performing neurofeedback training in these conditions would yield better results in performance than in silent ones. A total of forty-five student athletes aged from 18 to 35 years old and divided equally into three groups participated in the experiment (mean +/- SD for age: 22.02 +/- 3.05 years). The total neurofeedback session time for each subject was 300 min and were performed twice a week. The environment in which the neurofeedback sessions were conducted did not seem to have a significant impact on the training's success in terms of alpha relative amplitude changes (0.04 +/- 0.08 for silent room versus 0.07 +/- 0.28 for noisy room, p = 0.740). However, the group exposed to intermittent noise appears to have favourable results in all performance assessments (p = 0.005 for working memory and p = 0.003 for reaction time). The results of the study suggested that performing neurofeedback training in an environment with intermittent noise can be interesting to athletes. Nevertheless, it is imperative to perform a replicated crossover design.
- A fast simulated annealing algorithm for the examination timetabling problemPublication . Leite, Nuno; Melicio, Fernando; Rosa, AgostinhoThe timetabling problem involves the scheduling of a set of entities (e.g., lectures, exams, vehicles, or people) to a set of resources in a limited number of time slots, while satisfying a set of constraints. In this paper, a new variant of the simulated annealing (SA) algorithm, named FastSA, is proposed for solving the examination timetabling problem. In the FastSA's acceptance criterion, each exam selected for scheduling is only moved (and the associated move is evaluated) if that exam had any accepted moves in the immediately preceding temperature bin. Ten temperature bins were formed, ensuring that an equal number of evaluations is performed by the FastSA, in each bin. It was observed empirically that if an exam had zero accepted movements in the preceding temperature bin, it is likely to have few or zero accepted movements in the future, as it is becoming crystallised. Hence, the moves of all exams that are fixed along the way are not evaluated no more, yielding a lower number of evaluations compared to the reference algorithm, the standard SA. A saturation degree-based heuristic, coupled with Conflict-Based Statistics in order to avoid any exam assignment looping effect, is used to construct the initial solution. The proposed FastSA and the standard SA approaches were tested on the 2nd International Timetabling Competition (ITC 2007) benchmark set. Compared to the SA, the FastSA uses 17% less evaluations, on average, and a maximum of 41% less evaluations on one instance. In terms of solution cost, the FastSA is competitive with the SA algorithm attaining the best average fitness value in four out of twelve instances, while requiring less time to execute. In terms of average comparison with the state-of-the-art approaches, the FastSA improves on one out of twelve instances, and ranks third among the five best algorithms. The article's main impact comprises the points: (i) proposal of a new algorithm (FastSA) which is able to attain a reduced computation time (and number of evaluations computed) compared to the standard SA, (ii) demonstration of the FastSA capabilities on a NP-Complete timetabling problem, (iii) comparison with state-of-the-art approaches where the FastSA is able to improve the best known result on a benchmark instance. Due to the variety of problems solved by expert and intelligent systems using SA-based algorithms, we believe that the proposed approach will open new research paths with the creation of new algorithms that explore the space in a more efficient way.
- An exploratory study of training intensity in EEG neurofeedbackPublication . Esteves, Inês; Nan, Wenya; Alves, Cristiana P.; Calapez, Alexandre; Melicio, Fernando; Rosa, AgostinhoNeurofeedback training has shown benefits in clinical treatment and behavioral performance enhancement. Despite the wide range of applications, no consensus has been reached about the optimal training schedule. In this work, an EEG neurofeedback practical experiment was conducted aimed at investigating the effects of training intensity on the enhancement of the amplitude in the individual upper alpha band. We designed INTENSIVE and SPARSE training modalities, which differed regarding three essential aspects of training intensity: the number of sessions, the duration of a session, and the interval between sessions. Nine participants in the INTENSIVE group completed 4 sessions with 37.5 minutes each during consecutive days, while nine participants in the SPARSE group performed 6 sessions of 25 minutes spread over approximately 3 weeks. As a result, regarding the short-term effects, the upper alpha band amplitude change within sessions did not significantly differ between the two groups. Nonetheless, only the INTENSIVE group showed a significant increase in the upper alpha band amplitude. However, for the sustained effects across sessions, none of the groups showed significant changes in the upper alpha band amplitude across the whole course of training. The findings suggest that the progression within session is favored by the intensive design. Therefore, based on these findings, it is proposed that training intensity influences EEG self-regulation within sessions. Further investigations are needed to isolate different aspects of training intensity and effectively confirm if one modality globally outperforms the other.
- A cellular memetic algorithm for the examination timetabling problemPublication . Leite, Nuno; Fernandes, Carlos M.; Melicio, Fernando; Rosa, AgostinhoThe timetabling problem involves the scheduling of a set of entities (e.g., lectures, exams, vehicles, or people) to a given set of resources in a limited number of time slots, while satisfying a set of constraints. In this paper, a cellular memetic algorithm is proposed for solving the examination timetabling problem. Cellular evolutionary algorithms are population-based metaheuristics. They differ from non-cellular algorithms in that the population is organised in a cellular structure, providing for a smooth actualisation of the populations that contributes to improving the population diversity. The proposed cellular evolutionary algorithm is hybridised with the threshold acceptance local search metaheuristic. The implemented algorithm uses feasible genetic recombination and local search operators, thus limiting the exploration to the feasible solution space. The effect of the threshold acceptance used in the hybrid algorithm for the examination timetabling problem is studied. It is shown that a low threshold decreasing rate is needed in order to rearrange the most difficult exams in better periods, allowing for the easy set of exams to be placed in good periods as well. The approach was tested on the public Toronto and ITC 2007 benchmark sets. The proposed hybrid is able to attain four and three new upper bounds for the Toronto and ITC 2007 benchmark sets, respectively.