EEG signatures of low back and knee joint pain during movement execution: a short report

Chronic musculoskeletal pain has a high prevalence between European citizens, affecting their quality of life and their ability to work. The plastic changes associated with the occurrence of chronic musculoskeletal pain are still not fully understood. The current short report investigated the possible changes in brain activity caused by pain during movement in two of the most common musculoskeletal pain disorders in Denmark, knee pain and low back pain. Electroencephalography (EEG) was recorded from 20 participants (5 participants with knee pain, 5 with low back pain and 10 healthy controls). Participants with pain performed a movement that evoked pain in the area of interest, and the healthy controls performed the same movement. Electromyographic (EMG) signals were also collected to identify movement initiation. No differences were observed in brain activity of participants with pain and healthy controls during rest. During movement execution, though, participants with pain showed significantly higher event related synchronization in the alpha and beta bands compared to healthy controls. These changes could be related to higher cognitive processing, possibly due to the attempt of suppressing the pain. These results highlight the importance of assessing cortical activity during movement to reveal plastic changes due to musculoskeletal pain. This adds to our knowledge regarding plastic changes in cortical activity related to musculoskeletal pain in different locations. Such knowledge could help us identify neurophysiological markers for clinical changes and contribute to the development of new treatment approaches based on neuromodulation such as neurofeedback

Linked Fault Analysis

Numerous fault models have been developed, each with distinct characteristics and effects. These models should be evaluated in light of their costs, repeatability, and practicability. Moreover, there must be effective ways to use the injected fault to retrieve the secret key, especially if there are some countermeasures in the implementation. In this paper, we introduce a new fault analysis technique called ``linked fault analysis\u27\u27 (LFA), which can be viewed as a more powerful version of well-known fault attacks against implementations of symmetric primitives in various circumstances, especially software implementations. For known fault analyses, the bias over the faulty value or the relationship between the correct value and the faulty one, both produced by the fault injection serve as the foundations for the fault model. In the LFA, however, a single fault involves two intermediate values. The faulty target variable, $u\u27$, is linked to a second variable, $v$, such that a particular relation holds: $u\u27=l(v)$. We show that LFA lets the attacker perform fault attacks without the input control, with much fewer data than previously introduced fault attacks in the same class. Also, we show two approaches, called LDFA and LIFA, that show how LFA can be utilized in the presence or absence of typical redundant-based countermeasures. Finally, we demonstrate that LFA is still effective, but under specific circumstances, even when masking protections are in place. We performed our attacks against the public implementation of AES in ATMEGA328p to show how LFA works in the real world. The practical results and simulations validate our theoretical models as well