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Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies.
New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig.
Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included.
Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches.
Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.
The present study investigates how different emotions can alter social bargaining behavior. An important paradigm to study social bargaining is the Ultimatum Game. There, a proposer gets a pot of money and has to offer part of it to a responder. If the responder accepts, both players get the money as proposed by the proposer. If he rejects, none of the players gets anything. Rational choice models would predict that responders accept all offers above 0. However, evidence shows that responders typically reject a large proportion of all unfair offers. We analyzed participants’ behavior when they played the Ultimatum Game as responders and simultaneously collected electroencephalogram data in order to quantify the feedback-related negativity and P3b components. We induced state affect (momentarily emotions unrelated to the task) via short movie clips and measured trait affect (longer-lasting emotional dispositions) via questionnaires. State happiness led to increased acceptance rates of very unfair offers. Regarding neurophysiology, we found that unfair offers elicited larger feedback-related negativity amplitudes than fair offers. Additionally, an interaction of state and trait affect occurred: high trait negative affect (subsuming a variety of aversive mood states) led to increased feedback-related negativity amplitudes when participants were in an angry mood, but not if they currently experienced fear or happiness. We discuss that increased rumination might be responsible for this result, which might not occur, however, when people experience happiness or fear. Apart from that, we found that fair offers elicited larger P3b components than unfair offers, which might reflect increased pleasure in response to fair offers. Moreover, high trait negative affect was associated with decreased P3b amplitudes, potentially reflecting decreased motivation to engage in activities. We discuss implications of our results in the light of theories and research on depression and anxiety.
Gaze-independent brain-computer interfaces (BCIs) are a possible communication channel for persons with paralysis. We investigated if it is possible to use auditory stimuli to create a BCI for the Japanese Hiragana syllabary, which has 46 Hiragana characters. Additionally, we investigated if training has an effect on accuracy despite the high amount of different stimuli involved. Able-bodied participants (N = 6) were asked to select 25 syllables (out of fifty possible choices) using a two step procedure: First the consonant (ten choices) and then the vowel (five choices). This was repeated on 3 separate days. Additionally, a person with spinal cord injury (SCI) participated in the experiment. Four out of six healthy participants reached Hiragana syllable accuracies above 70% and the information transfer rate increased from 1.7 bits/min in the first session to 3.2 bits/min in the third session. The accuracy of the participant with SCI increased from 12% (0.2 bits/min) to 56% (2 bits/min) in session three. Reliable selections from a 10 × 5 matrix using auditory stimuli were possible and performance is increased by training. We were able to show that auditory P300 BCIs can be used for communication with up to fifty symbols. This enables the use of the technology of auditory P300 BCIs with a variety of applications.
Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.
Objective
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.
Methods
Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.
Results
Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.
Conclusions
Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.
Significance
Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Introduction
There is mounting evidence for the influence of emotional content on working memory performance. This is particularly important in light of the emotion processing that needs to take place when emotional content interferes with executive functions. In this study, we used emotional words of different valence but with similar arousal levels in an n-back task.
Methods
We examined the effects on activation in the prefrontal cortex by means of functional near-infrared spectroscopy (fNIRS) and on the late positive potential (LPP). FNIRS and LPP data were examined in 30 healthy subjects.
Results
Behavioral results show an influence of valence on the error rate depending on the difficulty of the task: more errors were made when the valence was negative and the task difficult. Brain activation was dependent both on the difficulty of the task and on the valence: negative valence of a word diminished the increase in activation, whereas positive valence did not influence the increase in activation, while difficulty levels increased. The LPP also differentiated between the different valences, and in addition was influenced by the task difficulty, the more difficult the task, the less differentiation could be observed.
Conclusions
Summarized, this study shows the influence of valence on a verbal working memory task. When a word contained a negative valence, the emotional content seemed to take precedence in contrast to words containing a positive valence. Working memory and emotion processing sites seemed to overlap and compete for resources even when words are carriers of the emotional content.
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.